chore: bump version to 4.9.1

feat: add trigger probability setting for TTS and support to render slider in schema (#4047 )
* feat: add trigger probability setting for TTS and support to render slider in schema * chore: ruff format
2025-12-15 16:07:10 +08:00 · 2025-12-15 16:04:27 +08:00 · 2025-12-15 15:33:40 +08:00 · 2025-12-15 15:27:42 +08:00 · 2025-12-15 11:47:21 +08:00 · 2025-12-14 21:44:12 +08:00
313 changed files with 20198 additions and 9534 deletions
@@ -1,6 +1,7 @@
 # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
 # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
 # github actions
+.git
 .github/
 .*ignore
 # User-specific stuff
@@ -19,4 +20,5 @@ data/
 changelogs/
 tests/
 .ruff_cache/
-.astrbot
+.astrbot
+astrbot.lock
@@ -16,7 +16,7 @@ body:

        请将插件信息填写到下方的 JSON 代码块中。其中 `tags`（插件标签）和 `social_link`（社交链接）选填。

-        不熟悉 JSON ？可以从 [此处](https://plugins.astrbot.app/submit) 生成 JSON ，生成后记得复制粘贴过来.
+        不熟悉 JSON ？可以从 [此站](https://plugins.astrbot.app) 右下角提交。

  - type: textarea
    id: plugin-info
@@ -1,46 +1,44 @@
-name: '🐛 报告 Bug'
+name: '🐛 Report Bug / 报告 Bug'
 title: '[Bug]'
-description: 提交报告帮助我们改进。
+description: Submit bug report to help us improve. / 提交报告帮助我们改进。
 labels: [ 'bug' ]
 body:
  - type: markdown
    attributes:
      value: |
-        感谢您抽出时间报告问题！请准确解释您的问题。如果可能，请提供一个可复现的片段（这有助于更快地解决问题）。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Thank you for taking the time to report this issue! Please describe your problem accurately. If possible, please provide a reproducible snippet (this will help resolve the issue more quickly). Please note that issues that are not detailed or have no logs will be closed immediately. Thank you for your understanding. / 感谢您抽出时间报告问题！请准确解释您的问题。如果可能，请提供一个可复现的片段（这有助于更快地解决问题）。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
  - type: textarea
    attributes:
-      label: 发生了什么
-      description: 描述你遇到的异常
+      label: What happened / 发生了什么
+      description: Description
      placeholder: >
-        一个清晰且具体的描述这个异常是什么。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Please provide a clear and specific description of what this exception is. Please note that issues that are not detailed or have no logs will be closed immediately. Thank you for your understanding. / 一个清晰且具体的描述这个异常是什么。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
    validations:
      required: true

  - type: textarea
    attributes:
-      label: 如何复现？
+      label: Reproduce / 如何复现？
      description: >
-        复现该问题的步骤
+        The steps to reproduce the issue. / 复现该问题的步骤
      placeholder: >
-        如: 1. 打开 '...'
+        Example: 1. Open '...'
    validations:
      required: true

  - type: textarea
    attributes:
-      label: AstrBot 版本、部署方式（如 Windows Docker Desktop 部署）、使用的提供商、使用的消息平台适配器
-      description: >
-        请提供您的 AstrBot 版本和部署方式。
+      label: AstrBot version, deployment method (e.g., Windows Docker Desktop deployment), provider used, and messaging platform used. / AstrBot 版本、部署方式（如 Windows Docker Desktop 部署）、使用的提供商、使用的消息平台适配器
      placeholder: >
-        如: 3.1.8 Docker, 3.1.7 Windows启动器
+        Example: 4.5.7 Docker, 3.1.7 Windows Launcher
    validations:
      required: true

  - type: dropdown
    attributes:
-      label: 操作系统
+      label: OS
      description: |
-        你在哪个操作系统上遇到了这个问题？
+        On which operating system did you encounter this problem? / 你在哪个操作系统上遇到了这个问题？
      multiple: false
      options:
        - 'Windows'
@@ -53,30 +51,30 @@ body:

  - type: textarea
    attributes:
-      label: 报错日志
+      label: Logs / 报错日志
      description: >
-        如报错日志、截图等。请提供完整的 Debug 级别的日志，不要介意它很长！请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Please provide complete Debug-level logs, such as error logs and screenshots. Don't worry if they're long! Please note that issues with insufficient details or no logs will be closed immediately. Thank you for your understanding. / 如报错日志、截图等。请提供完整的 Debug 级别的日志，不要介意它很长！请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
      placeholder: >
-        请提供完整的报错日志或截图。
+        Please provide a complete error log or screenshot. / 请提供完整的报错日志或截图。
    validations:
      required: true

  - type: checkboxes
    attributes:
-      label: 你愿意提交 PR 吗？
+      label: Are you willing to submit a PR? / 你愿意提交 PR 吗？
      description: >
-        这不是必需的，但我们很乐意在贡献过程中为您提供指导特别是如果你已经很好地理解了如何实现修复。
+        This is not required, but we would be happy to provide guidance during the contribution process, especially if you already have a good understanding of how to implement the fix. / 这不是必需的，但我们很乐意在贡献过程中为您提供指导特别是如果你已经很好地理解了如何实现修复。
      options:
-        - label: 是的，我愿意提交 PR!
+        - label: Yes!

  - type: checkboxes
    attributes:
      label: Code of Conduct
      options:
        - label: >
-            我已阅读并同意遵守该项目的 [行为准则](https://docs.github.com/zh/site-policy/github-terms/github-community-code-of-conduct)。
+            I have read and agree to abide by the project's [Code of Conduct](https://docs.github.com/zh/site-policy/github-terms/github-community-code-of-conduct)。
          required: true

  - type: markdown
    attributes:
-      value: "感谢您填写我们的表单！"
+      value: "Thank you for filling out our form! / 感谢您填写我们的表单！"
@@ -1,44 +1,25 @@
-<!-- 如果有的话，请指定此 PR 旨在解决的 ISSUE 编号。 -->
-<!-- If applicable, please specify the ISSUE number this PR aims to resolve. -->
-
-fixes #XYZ
-
---
-
-### Motivation / 动机
-
-<!--请描述此项更改的动机：它解决了什么问题？（例如：修复了 XX 错误，添加了 YY 功能）-->
-<!--Please describe the motivation for this change: What problem does it solve? (e.g., Fixes XX bug, adds YY feature)-->
+<!--Please describe the motivation for this change: What problem does it solve? (e.g., Fixes XX issue, adds YY feature)-->
+<!--请描述此项更改的动机：它解决了什么问题？（例如：修复了 XX issue，添加了 YY 功能）-->

 ### Modifications / 改动点

 <!--请总结你的改动：哪些核心文件被修改了？实现了什么功能？-->
 <!--Please summarize your changes: What core files were modified? What functionality was implemented?-->

-### Verification Steps / 验证步骤
-
-<!--请为审查者 (Reviewer) 提供清晰、可复现的验证步骤（例如：1. 导航到... 2. 点击...）。-->
-<!--Please provide clear and reproducible verification steps for the Reviewer (e.g., 1. Navigate to... 2. Click...).-->
+- [x] This is NOT a breaking change. / 这不是一个破坏性变更。
+<!-- If your changes is a breaking change, please uncheck the checkbox above -->

 ### Screenshots or Test Results / 运行截图或测试结果

-<!--请粘贴截图、GIF 或测试日志，作为执行“验证步骤”的证据，证明此改动有效。-->
 <!--Please paste screenshots, GIFs, or test logs here as evidence of executing the "Verification Steps" to prove this change is effective.-->
-
-### Compatibility & Breaking Changes / 兼容性与破坏性变更
-
-<!--请说明此变更的兼容性：哪些是破坏性变更？哪些地方做了向后兼容处理？是否提供了数据迁移方法？-->
-<!--Please explain the compatibility of this change: What are the breaking changes? What backward-compatible measures were taken? Are data migration paths provided?-->
-
- [ ] 这是一个破坏性变更 (Breaking Change)。/ This is a breaking change.
- [ ] 这不是一个破坏性变更。/ This is NOT a breaking change.
+<!--请粘贴截图、GIF 或测试日志，作为执行“验证步骤”的证据，证明此改动有效。-->

 ---

 ### Checklist / 检查清单

-<!--如果分支被合并，您的代码将服务于数万名用户！在提交前，请核查一下几点内容。-->
 <!--If merged, your code will serve tens of thousands of users! Please double-check the following items before submitting.-->
+<!--如果分支被合并，您的代码将服务于数万名用户！在提交前，请核查一下几点内容。-->

 - [ ] 😊 如果 PR 中有新加入的功能，已经通过 Issue / 邮件等方式和作者讨论过。/ If there are new features added in the PR, I have discussed it with the authors through issues/emails, etc.
 - [ ] 👀 我的更改经过了良好的测试，**并已在上方提供了“验证步骤”和“运行截图”**。/ My changes have been well-tested, **and "Verification Steps" and "Screenshots" have been provided above**.
@@ -13,7 +13,7 @@ jobs:
      contents: write
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: Dashboard Build
        run: |
@@ -70,7 +70,7 @@ jobs:
    needs: build-and-publish-to-github-release
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: Set up Python
        uses: actions/setup-python@v6
@@ -12,7 +12,7 @@ jobs:
    
    steps:
    - name: Checkout code
-      uses: actions/checkout@v5
+      uses: actions/checkout@v6
      
    - name: Set up Python
      uses: actions/setup-python@v6
@@ -56,7 +56,7 @@ jobs:
        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
    steps:
    - name: Checkout repository
-      uses: actions/checkout@v5
+      uses: actions/checkout@v6

    # Initializes the CodeQL tools for scanning.
    - name: Initialize CodeQL
@@ -17,7 +17,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
        with:
          fetch-depth: 0

@@ -11,7 +11,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: Setup Node.js
        uses: actions/setup-node@v6
@@ -3,18 +3,125 @@ name: Docker Image CI/CD
 on:
  push:
    tags:
-      - 'v*'
+      - "v*"
+  schedule:
+    # Run at 00:00 UTC every day
+    - cron: "0 0 * * *"
  workflow_dispatch:

 jobs:
-  publish-docker:
+  build-nightly-image:
+    if: github.event_name == 'schedule'
    runs-on: ubuntu-latest
+    env:
+      DOCKER_HUB_USERNAME: ${{ secrets.DOCKER_HUB_USERNAME }}
+      GHCR_OWNER: soulter
+      HAS_GHCR_TOKEN: ${{ secrets.GHCR_GITHUB_TOKEN != '' }}

    steps:
-      - name: Pull The Codes
-        uses: actions/checkout@v5
+      - name: Checkout
+        uses: actions/checkout@v6
        with:
-          fetch-depth: 0  # Must be 0 so we can fetch tags
+          fetch-depth: 1
+          fetch-tag: true
+
+      - name: Check for new commits today
+        if: github.event_name == 'schedule'
+        id: check-commits
+        run: |
+          # Get commits from the last 24 hours
+          commits=$(git log --since="24 hours ago" --oneline)
+          if [ -z "$commits" ]; then
+            echo "No commits in the last 24 hours, skipping build"
+            echo "has_commits=false" >> $GITHUB_OUTPUT
+          else
+            echo "Found commits in the last 24 hours:"
+            echo "$commits"
+            echo "has_commits=true" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Exit if no commits
+        if: github.event_name == 'schedule' && steps.check-commits.outputs.has_commits == 'false'
+        run: exit 0
+
+      - name: Build Dashboard
+        run: |
+          cd dashboard
+          npm install
+          npm run build
+          mkdir -p dist/assets
+          echo $(git rev-parse HEAD) > dist/assets/version
+          cd ..
+          mkdir -p data
+          cp -r dashboard/dist data/
+
+      - name: Determine test image tags
+        id: test-meta
+        run: |
+          short_sha=$(echo "${GITHUB_SHA}" | cut -c1-12)
+          build_date=$(date +%Y%m%d)
+          echo "short_sha=$short_sha" >> $GITHUB_OUTPUT
+          echo "build_date=$build_date" >> $GITHUB_OUTPUT
+
+      - name: Set QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_HUB_USERNAME }}
+          password: ${{ secrets.DOCKER_HUB_PASSWORD }}
+
+      - name: Login to GitHub Container Registry
+        if: env.HAS_GHCR_TOKEN == 'true'
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ env.GHCR_OWNER }}
+          password: ${{ secrets.GHCR_GITHUB_TOKEN }}
+
+      - name: Build nightly image tags list
+        id: test-tags
+        run: |
+          TAGS="${{ env.DOCKER_HUB_USERNAME }}/astrbot:nightly-latest
+          ${{ env.DOCKER_HUB_USERNAME }}/astrbot:nightly-${{ steps.test-meta.outputs.build_date }}-${{ steps.test-meta.outputs.short_sha }}"
+          if [ "${{ env.HAS_GHCR_TOKEN }}" = "true" ]; then
+            TAGS="$TAGS
+          ghcr.io/${{ env.GHCR_OWNER }}/astrbot:nightly-latest
+          ghcr.io/${{ env.GHCR_OWNER }}/astrbot:nightly-${{ steps.test-meta.outputs.build_date }}-${{ steps.test-meta.outputs.short_sha }}"
+          fi
+          echo "tags<<EOF" >> $GITHUB_OUTPUT
+          echo "$TAGS" >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
+
+      - name: Build and Push Nightly Image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ steps.test-tags.outputs.tags }}
+
+      - name: Post build notifications
+        run: echo "Test Docker image has been built and pushed successfully"
+
+  build-release-image:
+    if: github.event_name == 'workflow_dispatch' || (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v'))
+    runs-on: ubuntu-latest
+    env:
+      DOCKER_HUB_USERNAME: ${{ secrets.DOCKER_HUB_USERNAME }}
+      GHCR_OWNER: soulter
+      HAS_GHCR_TOKEN: ${{ secrets.GHCR_GITHUB_TOKEN != '' }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 1
+          fetch-tag: true

      - name: Get latest tag (only on manual trigger)
        id: get-latest-tag
@@ -27,21 +134,22 @@ jobs:
        if: github.event_name == 'workflow_dispatch'
        run: git checkout ${{ steps.get-latest-tag.outputs.latest_tag }}

-      - name: Check if version is pre-release
-        id: check-prerelease
+      - name: Compute release metadata
+        id: release-meta
        run: |
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
+          if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
            version="${{ steps.get-latest-tag.outputs.latest_tag }}"
          else
-            version="${{ github.ref_name }}"
+            version="${GITHUB_REF#refs/tags/}"
          fi
          if [[ "$version" == *"beta"* ]] || [[ "$version" == *"alpha"* ]]; then
            echo "is_prerelease=true" >> $GITHUB_OUTPUT
-            echo "Version $version is a pre-release, will not push latest tag"
+            echo "Version $version marked as pre-release"
          else
            echo "is_prerelease=false" >> $GITHUB_OUTPUT
-            echo "Version $version is a stable release, will push latest tag"
+            echo "Version $version marked as stable"
          fi
+          echo "version=$version" >> $GITHUB_OUTPUT

      - name: Build Dashboard
        run: |
@@ -67,23 +175,24 @@ jobs:
          password: ${{ secrets.DOCKER_HUB_PASSWORD }}

      - name: Login to GitHub Container Registry
+        if: env.HAS_GHCR_TOKEN == 'true'
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
-          username: Soulter
+          username: ${{ env.GHCR_OWNER }}
          password: ${{ secrets.GHCR_GITHUB_TOKEN }}

-      - name: Build and Push Docker to DockerHub and Github GHCR
+      - name: Build and Push Release Image
        uses: docker/build-push-action@v6
        with:
          context: .
          platforms: linux/amd64,linux/arm64
          push: true
          tags: |
-            ${{ steps.check-prerelease.outputs.is_prerelease == 'false' && format('{0}/astrbot:latest', secrets.DOCKER_HUB_USERNAME) || '' }}
-            ${{ secrets.DOCKER_HUB_USERNAME }}/astrbot:${{ github.event_name == 'workflow_dispatch' && steps.get-latest-tag.outputs.latest_tag || github.ref_name }}
-            ${{ steps.check-prerelease.outputs.is_prerelease == 'false' && 'ghcr.io/soulter/astrbot:latest' || '' }}
-            ghcr.io/soulter/astrbot:${{ github.event_name == 'workflow_dispatch' && steps.get-latest-tag.outputs.latest_tag || github.ref_name }}
+            ${{ steps.release-meta.outputs.is_prerelease == 'false' && format('{0}/astrbot:latest', env.DOCKER_HUB_USERNAME) || '' }}
+            ${{ steps.release-meta.outputs.is_prerelease == 'false' && env.HAS_GHCR_TOKEN == 'true' && format('ghcr.io/{0}/astrbot:latest', env.GHCR_OWNER) || '' }}
+            ${{ format('{0}/astrbot:{1}', env.DOCKER_HUB_USERNAME, steps.release-meta.outputs.version) }}
+            ${{ env.HAS_GHCR_TOKEN == 'true' && format('ghcr.io/{0}/astrbot:{1}', env.GHCR_OWNER, steps.release-meta.outputs.version) || '' }}

      - name: Post build notifications
-        run: echo "Docker image has been built and pushed successfully"
+        run: echo "Release Docker image has been built and pushed successfully"
@@ -0,0 +1,58 @@
+name: Smoke Test
+
+on:
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'README*.md'
+      - 'changelogs/**'
+      - 'dashboard/**'
+  pull_request:
+  workflow_dispatch:
+
+jobs:
+  smoke-test:
+    name: Run smoke tests
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.12'
+      
+      - name: Install UV package manager
+        run: |
+          pip install uv
+
+      - name: Install dependencies
+        run: |
+          uv sync
+        timeout-minutes: 15
+
+      - name: Run smoke tests
+        run: |
+          uv run main.py &
+          APP_PID=$!
+
+          echo "Waiting for application to start..."
+          for i in {1..60}; do
+            if curl -f http://localhost:6185 > /dev/null 2>&1; then
+              echo "Application started successfully!"
+              kill $APP_PID
+              exit 0
+            fi
+            sleep 1
+          done
+
+          echo "Application failed to start within 30 seconds"
+          kill $APP_PID 2>/dev/null || true
+          exit 1
+        timeout-minutes: 2
@@ -34,6 +34,7 @@ dashboard/node_modules/
 dashboard/dist/
 package-lock.json
 package.json
+yarn.lock

 # Operating System
 **/.DS_Store
@@ -47,3 +48,5 @@ astrbot.lock
 chroma
 venv/*
 pytest.ini
+AGENTS.md
+IFLOW.md
@@ -0,0 +1,65 @@
+# CONTRIBUTING
+
+## 贡献指南
+
+首先，感谢您花时间做出贡献！❤️
+
+所有类型的贡献都受到鼓励和重视。有关不同的帮助方式和处理方式的详细信息，请参阅[目录](#目录)。在做出贡献之前，请确保阅读相关部分。这将使我们维护人员的工作变得更加容易，并为所有参与者带来顺畅的体验。社区期待您的贡献。🎉
+
+### 目录
+
+- [报告问题](#报告问题)
+- [提交代码更改](#提交代码更改)
+
+### 报告问题
+
+如果您在使用 AstrBot 时遇到任何问题，请按照以下步骤报告：
+
+1. **检查现有问题**：在提交新问题之前，请先检查 [Issues](https://github.com/AstrBotDevs/AstrBot/issues) 中是否已经存在类似的问题。
+2. **创建新问题**：如果没有类似的问题，请创建一个新问题。请确保提供以下信息：
+   - 问题的简要描述
+   - 重现问题的步骤
+   - 预期结果和实际结果
+   - 相关日志或错误消息
+
+### 提交代码更改
+
+#### 分支命名
+
+我们使用 `fix/` 前缀来修复错误，使用 `feat/` 前缀来添加新功能。对于 `fix/` 分支，请使用简短的描述，或者直接使用 Issue 编号。例如：`fix/1234` 或者 `fix/1234-login-typo`。对于 `feat/` 分支，请使用简短的描述，例如：`feat/add-user-profile`。
+
+#### PR 描述
+
+- 请使用英文描述您的 PR。
+- 标题请使用 `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` 等语义化前缀，并简要描述更改内容。如：`fix: correct login page typo`。
+
+## Contributing Guide
+
+First off, thanks for taking the time to contribute! ❤️
+
+All types of contributions are encouraged and valued. See the [Table of Contents](#table-of-contents) for different ways to help and details about how this project handles them. Please make sure to read the relevant section before making your contribution. It will make it a lot easier for us maintainers and smooth out the experience for all involved. The community looks forward to your contributions. 🎉
+
+### Table of Contents
+
+- [Reporting Issues](#reporting-issues)
+- [Pull Requests](#pull-requests)
+
+### Reporting Issues
+
+If you encounter any issues while using AstrBot, please follow these steps to report them:
+1. **Check Existing Issues**: Before submitting a new issue, please check if a similar issue already exists in the [Issues](https://github.com/AstrBotDevs/AstrBot/issues) section of the repository.
+2. **Create a New Issue**: If no similar issue exists, please create a new issue. Make sure to provide the following information:
+   - A brief description of the issue
+   - Steps to reproduce the issue
+   - Expected and actual results
+   - Relevant logs or error messages
+
+### Pull Requests
+
+#### Branch Naming
+
+We use the `fix/` prefix for bug fixes and the `feat/` prefix for new features. For `fix/` branches, please use a short description or directly use the Issue number, e.g., `fix/1234` or `fix/1234-login-typo`. For `feat/` branches, please use a short description, e.g., `feat/add-user-profile`.
+
+#### PR Description
+- Please use English to describe your PR.
+- Use semantic prefixes like `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` in the title, followed by a brief description of the changes, e.g., `fix: correct login page typo`.
@@ -18,15 +18,15 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

-RUN curl -fsSL https://deb.nodesource.com/setup_lts.x | bash - && \
-    apt-get install -y --no-install-recommends nodejs && \
-    echo "3.11" > .python-version && \
-    rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y curl gnupg \
+    && curl -fsSL https://deb.nodesource.com/setup_lts.x | bash - \
+    && apt-get install -y nodejs

-RUN python -m pip install --no-cache-dir uv && \
-    uv pip install socksio pilk --no-cache-dir --system
+RUN python -m pip install uv \
+    && echo "3.11" > .python-version
+RUN uv pip install -r requirements.txt --no-cache-dir --system
+RUN uv pip install socksio uv pilk --no-cache-dir --system

 EXPOSE 6185
-EXPOSE 6186

-CMD ["uv", "run", "main.py"]
+CMD ["python", "main.py"]
@@ -1,40 +0,0 @@
-FROM python:3.11-slim
-
-WORKDIR /AstrBot
-
-COPY . /AstrBot/
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    gcc \
-    build-essential \
-    python3-dev \
-    libffi-dev \
-    libssl-dev \
-    curl \
-    unzip \
-    ca-certificates \
-    bash \
-    git \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-ENV NVM_DIR="/root/.nvm" \
-    NODE_VERSION=22
-RUN curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.2/install.sh | bash && \
-    . "$NVM_DIR/nvm.sh" && \
-    nvm install $NODE_VERSION && \
-    nvm use $NODE_VERSION && \
-    nvm alias default $NODE_VERSION && \
-    node -v && npm -v && \
-    echo "3.11" > .python-version
-ENV PATH="$NVM_DIR/versions/node/v$(node -v | cut -d 'v' -f 2)/bin:$PATH"
-
-RUN python -m pip install --no-cache-dir uv
-
-# 安装项目依赖（根据指南，使用 uv sync）
-RUN uv sync --no-cache
-
-EXPOSE 6185
-EXPOSE 6186
-
-CMD ["uv", "run", "main.py"]
@@ -1,48 +1,54 @@
 ![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)

-</p>
-
 <div align="center">

-<br>
-
-<div>
-<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=1" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
-</div>
-
-<br>
-
-<div>
-<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
-<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
-<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
-<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
-<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
-<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&style=for-the-badge&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600">
-</div>
-
-<br>

 <a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
 <a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>
+
+<div>
+<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+</div>
+
+<br>
+
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
+<img src="https://deepwiki.com/badge.svg" href="https://deepwiki.com/AstrBotDevs/AstrBot">
+<a href="https://zread.ai/AstrBotDevs/AstrBot" target="_blank"><img src="https://img.shields.io/badge/Ask_Zread-_.svg?style=flat&color=00b0aa&labelColor=000000&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTQuOTYxNTYgMS42MDAxSDIuMjQxNTZDMS44ODgxIDEuNjAwMSAxLjYwMTU2IDEuODg2NjQgMS42MDE1NiAyLjI0MDFWNC45NjAxQzEuNjAxNTYgNS4zMTM1NiAxLjg4ODEgNS42MDAxIDIuMjQxNTYgNS42MDAxSDQuOTYxNTZDNS4zMTUwMiA1LjYwMDEgNS42MDE1NiA1LjMxMzU2IDUuNjAxNTYgNC45NjAxVjIuMjQwMUM1LjYwMTU2IDEuODg2NjQgNS4zMTUwMiAxLjYwMDEgNC45NjE1NiAxLjYwMDFaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00Ljk2MTU2IDEwLjM5OTlIMi4yNDE1NkMxLjg4ODEgMTAuMzk5OSAxLjYwMTU2IDEwLjY4NjQgMS42MDE1NiAxMS4wMzk5VjEzLjc1OTlDMS42MDE1NiAxNC4xMTM0IDEuODg4MSAxNC4zOTk5IDIuMjQxNTYgMTQuMzk5OUg0Ljk2MTU2QzUuMzE1MDIgMTQuMzk5OSA1LjYwMTU2IDE0LjExMzQgNS42MDE1NiAxMy43NTk5VjExLjAzOTlDNS42MDE1NiAxMC42ODY0IDUuMzE1MDIgMTAuMzk5OSA0Ljk2MTU2IDEwLjM5OTlaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik0xMy43NTg0IDEuNjAwMUgxMS4wMzg0QzEwLjY4NSAxLjYwMDEgMTAuMzk4NCAxLjg4NjY0IDEwLjM5ODQgMi4yNDAxVjQuOTYwMUMxMC4zOTg0IDUuMzEzNTYgMTAuNjg1IDUuNjAwMSAxMS4wMzg0IDUuNjAwMUgxMy43NTg0QzE0LjExMTkgNS42MDAxIDE0LjM5ODQgNS4zMTM1NiAxNC4zOTg0IDQuOTYwMVYyLjI0MDFDMTQuMzk4NCAxLjg4NjY0IDE0LjExMTkgMS42MDAxIDEzLjc1ODQgMS42MDAxWiIgZmlsbD0iI2ZmZiIvPgo8cGF0aCBkPSJNNCAxMkwxMiA0TDQgMTJaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00IDEyTDEyIDQiIHN0cm9rZT0iI2ZmZiIgc3Ryb2tlLXdpZHRoPSIxLjUiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIvPgo8L3N2Zz4K&logoColor=ffffff" alt="zread"/></a>
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?color=76bad9"/></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600">
+<img src="https://gitcode.com/Soulter/AstrBot/star/badge.svg" href="https://gitcode.com/Soulter/AstrBot">
+</div>
+
+<br>
+
 <a href="https://astrbot.app/">文档</a> ｜
 <a href="https://blog.astrbot.app/">Blog</a> ｜
 <a href="https://astrbot.featurebase.app/roadmap">路线图</a> ｜
 <a href="https://github.com/AstrBotDevs/AstrBot/issues">问题提交</a>
 </div>

-AstrBot 是一个开源的一站式 Agent 聊天机器人平台及开发框架。
+AstrBot 是一个开源的一站式 Agent 聊天机器人平台，可接入主流即时通讯软件，为个人、开发者和团队打造可靠、可扩展的对话式智能基础设施。无论是个人 AI 伙伴、智能客服、自动化助手，还是企业知识库，AstrBot 都能在你的即时通讯软件平台的工作流中快速构建生产可用的 AI 应用。
+
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />

 ## 主要功能

-1. **大模型对话**。支持接入多种大模型服务。支持多模态、工具调用、MCP、原生知识库、人设等功能。
-2. **多消息平台支持**。支持接入 QQ、企业微信、微信公众号、飞书、Telegram、钉钉、Discord、KOOK 等平台。支持速率限制、白名单、百度内容审核。
-3. **Agent**。完善适配的 Agentic 能力。支持多轮工具调用、内置沙盒代码执行器、网页搜索等功能。
-4. **插件扩展**。深度优化的插件机制，支持[开发插件](https://astrbot.app/dev/plugin.html)扩展功能，社区插件生态丰富。
-5. **WebUI**。可视化配置和管理机器人，功能齐全。
+1. 💯 免费 & 开源。
+1. ✨ AI 大模型对话，多模态，Agent，MCP，知识库，人格设定。
+2. 🤖 支持接入 Dify、阿里云百炼、Coze 等智能体平台。
+2. 🌐 多平台，支持 QQ、企业微信、飞书、钉钉、微信公众号、Telegram、Slack 以及[更多](#支持的消息平台)。
+3. 📦 插件扩展，已有近 800 个插件可一键安装。
+5. 💻 WebUI 支持。
+6. 🌐 国际化（i18n）支持。

-## 部署方式
+## 快速开始

 #### Docker 部署(推荐 🥳)

@@ -50,6 +56,12 @@ AstrBot 是一个开源的一站式 Agent 聊天机器人平台及开发框架

 请参阅官方文档 [使用 Docker 部署 AstrBot](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) 。

+#### uv 部署
+
+```bash
+uvx astrbot
+```
+
 #### 宝塔面板部署

 AstrBot 与宝塔面板合作，已上架至宝塔面板。
@@ -101,101 +113,73 @@ uv run main.py

 或者请参阅官方文档 [通过源码部署 AstrBot](https://astrbot.app/deploy/astrbot/cli.html) 。

-## 🌍 社区
-
-### QQ 群组
-
- 1 群：322154837
- 3 群：630166526
- 5 群：822130018
- 6 群：753075035
- 开发者群：975206796
-
-### Telegram 群组
-
-<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
-
-### Discord 群组
-
-<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
-
-## ⚡ 消息平台支持情况
+## 支持的消息平台

 **官方维护**

-| 平台    | 支持性 |
-| -------- | ------- |
-| QQ(官方平台) | ✔    |
-| QQ(OneBot)      | ✔    |
-| Telegram   | ✔    |
-| 企微应用    | ✔    |
-| 企微智能机器人 | ✔ |
-| 微信客服    | ✔    |
-| 微信公众号    | ✔    |
-| 飞书   | ✔    |
-| 钉钉   | ✔    |
-| Slack   | ✔    |
-| Discord   | ✔    |
-| Satori   | ✔    |
-| Misskey   | ✔    |
-| Whatsapp | 将支持 |
-| LINE | 将支持 |
+- QQ (官方平台 & OneBot)
+- Telegram
+- 企微应用 & 企微智能机器人
+- 微信客服 & 微信公众号
+- 飞书
+- 钉钉
+- Slack
+- Discord
+- Satori
+- Misskey
+- Whatsapp (将支持)
+- LINE (将支持)

 **社区维护**

-| 平台    | 支持性 |
-| -------- | ------- |
-| [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)   | ✔    |
-| [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)   | ✔    |
-| [Bilibili 私信](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)   | ✔    |
-| [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)   | ✔    |
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili 私信](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)

-## ⚡ 提供商支持情况
+## 支持的模型服务

 **大模型服务**

-| 名称  | 支持性 | 备注 |
-| -------- | ------- | ------- |
-| OpenAI | ✔    | 支持任何兼容 OpenAI API 的服务 |
-| Anthropic | ✔    |  |
-| Google Gemini | ✔   |  |
-| Moonshot AI | ✔   |  |
-| 智谱 AI | ✔   |  |
-| DeepSeek | ✔   |  |
-| Ollama | ✔     | 本地部署 DeepSeek 等开源语言模型 |
-| LM Studio | ✔    | 本地部署 DeepSeek 等开源语言模型 |
-| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74) | ✔  |  |
-| [302.AI](https://share.302.ai/rr1M3l) | ✔   |  |
-| [小马算力](https://www.tokenpony.cn/3YPyf) | ✔   |  |
-| 硅基流动 | ✔    |  |
-| PPIO 派欧云 | ✔    |  |
-| ModelScope | ✔    |  |
-| OneAPI | ✔    |  |
-| Dify | ✔    |  |
-| 阿里云百炼应用 | ✔    |  |
-| Coze | ✔    |  |
+- OpenAI 及兼容服务
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- 智谱 AI
+- DeepSeek
+- Ollama (本地部署)
+- LM Studio (本地部署)
+- [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [小马算力](https://www.tokenpony.cn/3YPyf)
+- [硅基流动](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
+- [PPIO 派欧云](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI
+
+**LLMOps 平台**
+
+- Dify
+- 阿里云百炼应用
+- Coze

 **语音转文本服务**

-| 名称  | 支持性 | 备注 |
-| -------- | ------- | ------- |
-| Whisper | ✔    | 支持 API、本地部署 |
-| SenseVoice | ✔   | 本地部署 |
+- OpenAI Whisper
+- SenseVoice

 **文本转语音服务**

-| 名称  | 支持性 | 备注 |
-| -------- | ------- | ------- |
-| OpenAI TTS | ✔    |  |
-| Gemini TTS | ✔    |  |
-| GSVI | ✔    | GPT-Sovits-Inference |
-| GPT-SoVITs | ✔     | GPT-Sovits |
-| FishAudio | ✔    | |
-| Edge TTS | ✔    | Edge 浏览器的免费 TTS |
-| 阿里云百炼 TTS | ✔    |  |
-| Azure TTS | ✔   | |
-| Minimax TTS | ✔   | |
-| 火山引擎 TTS | ✔   | |
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- 阿里云百炼 TTS
+- Azure TTS
+- Minimax TTS
+- 火山引擎 TTS

 ## ❤️ 贡献

@@ -215,6 +199,25 @@ pip install pre-commit
 pre-commit install
 ```

+## 🌍 社区
+
+### QQ 群组
+
+- 1 群：322154837
+- 3 群：630166526
+- 5 群：822130018
+- 6 群：753075035
+- 7 群：743746109
+- 开发者群：975206796
+
+### Telegram 群组
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Discord 群组
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
 ## ❤️ Special Thanks

 特别感谢所有 Contributors 和插件开发者对 AstrBot 的贡献 ❤️
@@ -229,7 +232,7 @@ pre-commit install

 ## ⭐ Star History

-> [!TIP] 
+> [!TIP]
 > 如果本项目对您的生活 / 工作产生了帮助，或者您关注本项目的未来发展，请给项目 Star，这是我们维护这个开源项目的动力 <3

 <div align="center">
@@ -240,4 +243,10 @@ pre-commit install

 </details>

+<div align="center">
+
 _私は、高性能ですから!_
+
+<img src="https://files.astrbot.app/watashiwa-koseino-desukara.gif" width="100"/>
+</div
+
@@ -1,182 +1,247 @@
-<p align="center">
-    
-![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)

 </p>

 <div align="center">

-_✨ Easy-to-use Multi-platform LLM Chatbot & Development Framework ✨_
+<br>

+<div>
 <a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/AstrBotDevs/AstrBot)](https://github.com/AstrBotDevs/AstrBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
-<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot"/></a>
-<a  href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="Static Badge" src="https://img.shields.io/badge/QQ群-630166526-purple"></a>
-[![wakatime](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e.svg)](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e)
-![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7%E6%97%A5%E6%B6%88%E6%81%AF%E4%B8%8A%E8%A1%8C%E9%87%8F&cacheSeconds=3600)
-[![codecov](https://codecov.io/gh/AstrBotDevs/AstrBot/graph/badge.svg?token=FF3P5967B8)](https://codecov.io/gh/AstrBotDevs/AstrBot)
-
-<a href="https://astrbot.app/">Documentation</a> ｜
-<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue Tracking</a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 </div>

-AstrBot is a loosely coupled, asynchronous chatbot and development framework that supports multi-platform deployment, featuring an easy-to-use plugin system and comprehensive Large Language Model (LLM) integration capabilities.
+<br>

-## ✨ Key Features
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%20plugins&style=for-the-badge&label=Marketplace&cacheSeconds=3600">
+</div>

-1. **LLM Conversations** - Supports various LLMs including OpenAI API, Google Gemini, Llama, Deepseek, ChatGLM, etc. Enables local model deployment via Ollama/LLMTuner. Features multi-turn dialogues, personality contexts, multimodal capabilities (image understanding), and speech-to-text (Whisper).
-2. **Multi-platform Integration** - Supports QQ (OneBot), QQ Channels, WeChat (Gewechat), Feishu, and Telegram. Planned support for DingTalk, Discord, WhatsApp, and Xiaomi Smart Speakers. Includes rate limiting, whitelisting, keyword filtering, and Baidu content moderation.
-3. **Agent Capabilities** - Native support for code execution, natural language TODO lists, web search. Integrates with [Dify Platform](https://dify.ai/) for easy access to Dify assistants/knowledge bases/workflows.
-4. **Plugin System** - Optimized plugin mechanism with minimal development effort. Supports multiple installed plugins.
-5. **Web Dashboard** - Visual configuration management, plugin controls, logging, and WebChat interface for direct LLM interaction.
-6. **High Stability & Modularity** - Event bus and pipeline architecture ensures high modularization and loose coupling.
+<br>

-> [!TIP]
-> Dashboard Demo: [https://demo.astrbot.app/](https://demo.astrbot.app/)  
-> Username: `astrbot`, Password: `astrbot` (LLM not configured for chat page)
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>

-## ✨ Deployment
+<a href="https://astrbot.app/">Documentation</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">Roadmap</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue Tracker</a>
+</div>

-#### Docker Deployment
+AstrBot is an open-source all-in-one Agent chatbot platform that integrates with mainstream instant messaging apps. It provides reliable and scalable conversational AI infrastructure for individuals, developers, and teams. Whether you're building a personal AI companion, intelligent customer service, automation assistant, or enterprise knowledge base, AstrBot enables you to quickly build production-ready AI applications within your IM platform workflows.

-See docs: [Deploy with Docker](https://astrbot.app/deploy/astrbot/docker.html#docker-deployment)
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />

-#### Windows Installer
+## Key Features

-Requires Python (>3.10). See docs: [Windows Installer Guide](https://astrbot.app/deploy/astrbot/windows.html)
+1. 💯 Free & Open Source.
+2. ✨ AI LLM Conversations, Multimodal, Agent, MCP, Knowledge Base, Persona Settings.
+3. 🤖 Supports integration with Dify, Alibaba Cloud Bailian, Coze and other agent platforms.
+4. 🌐 Multi-Platform: QQ, WeChat Work, Feishu, DingTalk, WeChat Official Accounts, Telegram, Slack, and [more](#supported-messaging-platforms).
+5. 📦 Plugin Extensions with nearly 800 plugins available for one-click installation.
+6. 💻 WebUI Support.
+7. 🌐 Internationalization (i18n) Support.

-#### Replit Deployment
+## Quick Start
+
+#### Docker Deployment (Recommended 🥳)
+
+We recommend deploying AstrBot using Docker or Docker Compose.
+
+Please refer to the official documentation: [Deploy AstrBot with Docker](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot).
+
+#### uv Deployment
+
+```bash
+uvx astrbot
+```
+
+#### BT-Panel Deployment
+
+AstrBot has partnered with BT-Panel and is now available in their marketplace.
+
+Please refer to the official documentation: [BT-Panel Deployment](https://astrbot.app/deploy/astrbot/btpanel.html).
+
+#### 1Panel Deployment
+
+AstrBot has been officially listed on the 1Panel marketplace.
+
+Please refer to the official documentation: [1Panel Deployment](https://astrbot.app/deploy/astrbot/1panel.html).
+
+#### Deploy on RainYun
+
+AstrBot has been officially listed on RainYun's cloud application platform with one-click deployment.
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Deploy on Replit
+
+Community-contributed deployment method.

 [![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)

+#### Windows One-Click Installer
+
+Please refer to the official documentation: [Deploy AstrBot with Windows One-Click Installer](https://astrbot.app/deploy/astrbot/windows.html).
+
 #### CasaOS Deployment

-Community-contributed method.  
-See docs: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html)
+Community-contributed deployment method.
+
+Please refer to the official documentation: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html).

 #### Manual Deployment

-See docs: [Source Code Deployment](https://astrbot.app/deploy/astrbot/cli.html)
+First, install uv:

-## ⚡ Platform Support
+```bash
+pip install uv
+```

-| Platform                                                       | Status | Details             | Message Types       |
-| -------------------------------------------------------------- | ------ | ------------------- | ------------------- |
-| QQ (Official Bot)                                              | ✔      | Private/Group chats | Text, Images        |
-| QQ (OneBot)                                                    | ✔      | Private/Group chats | Text, Images, Voice |
-| WeChat (Personal)                                              | ✔      | Private/Group chats | Text, Images, Voice |
-| [Telegram](https://github.com/AstrBotDevs/AstrBot_plugin_telegram) | ✔      | Private/Group chats | Text, Images        |
-| [WeChat Work](https://github.com/AstrBotDevs/AstrBot_plugin_wecom) | ✔      | Private chats       | Text, Images, Voice |
-| Feishu                                                         | ✔      | Group chats         | Text, Images        |
-| WeChat Open Platform                                           | 🚧      | Planned             | -                   |
-| Discord                                                        | 🚧      | Planned             | -                   |
-| WhatsApp                                                       | 🚧      | Planned             | -                   |
-| Xiaomi Speakers                                                | 🚧      | Planned             | -                   |
+Install AstrBot via Git Clone:

-## Provider Support Status
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```

-| Name                      | Support | Type                   | Notes                                                                 |
-|---------------------------|---------|------------------------|-----------------------------------------------------------------------|
-| OpenAI API                | ✔       | Text Generation        | Supports all OpenAI API-compatible services including DeepSeek, Google Gemini, GLM, Moonshot, Alibaba Cloud Bailian, Silicon Flow, xAI, etc. |
-| Claude API                | ✔       | Text Generation        |                                                                       |
-| Google Gemini API         | ✔       | Text Generation        |                                                                       |
-| Dify                      | ✔       | LLMOps                 |                                                                       |
-| DashScope (Alibaba Cloud) | ✔       | LLMOps                 |                                                                       |
-| Ollama                    | ✔       | Model Loader           | Local deployment for open-source LLMs (DeepSeek, Llama, etc.)         |
-| LM Studio                 | ✔       | Model Loader           | Local deployment for open-source LLMs (DeepSeek, Llama, etc.)         |
-| LLMTuner                  | ✔       | Model Loader           | Local loading of fine-tuned models (e.g. LoRA)                        |
-| OneAPI                    | ✔       | LLM Distribution       |                                                                       |
-| Whisper                   | ✔       | Speech-to-Text         | Supports API and local deployment                                    |
-| SenseVoice                | ✔       | Speech-to-Text         | Local deployment                                                     |
-| OpenAI TTS API            | ✔       | Text-to-Speech         |                                                                       |
-| Fishaudio                 | ✔       | Text-to-Speech         | Project involving GPT-Sovits author                                  |
+Or refer to the official documentation: [Deploy AstrBot from Source](https://astrbot.app/deploy/astrbot/cli.html).

-# 🦌 Roadmap
+## Supported Messaging Platforms

-> [!TIP]
-> Suggestions welcome via Issues <3
+**Officially Maintained**

- [ ] Ensure feature parity across all platform adapters
- [ ] Optimize plugin APIs
- [ ] Add default TTS services (e.g., GPT-Sovits)
- [ ] Enhance chat features with persistent memory
- [ ] i18n Planning
+- QQ (Official Platform & OneBot)
+- Telegram
+- WeChat Work Application & WeChat Work Intelligent Bot
+- WeChat Customer Service & WeChat Official Accounts
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (Coming Soon)
+- LINE (Coming Soon)

-## ❤️ Contributions
+**Community Maintained**

-All Issues/PRs welcome! Simply submit your changes to this project :)
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili Direct Messages](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)

-For major features, please discuss via Issues first.
+## Supported Model Services

-## 🌟 Support
+**LLM Services**

- Star this project!
- Support via [Afdian](https://afdian.com/a/soulter)
- WeChat support: [QR Code](https://drive.soulter.top/f/pYfA/d903f4fa49a496fda3f16d2be9e023b5.png)
+- OpenAI and Compatible Services
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- Zhipu AI
+- DeepSeek
+- Ollama (Self-hosted)
+- LM Studio (Self-hosted)
+- [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [TokenPony](https://www.tokenpony.cn/3YPyf)
+- [SiliconFlow](https://docs.siliconflow.cn/cn/usecases/use-siliconcloud-in-astrbot)
+- [PPIO Cloud](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI

-## ✨ Demos
+**LLMOps Platforms**

-> [!NOTE]
-> Code executor file I/O currently tested with Napcat(QQ)/Lagrange(QQ)
+- Dify
+- Alibaba Cloud Bailian Applications
+- Coze

-<div align='center'>
+**Speech-to-Text Services**

-<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
+- OpenAI Whisper
+- SenseVoice

-_✨ Docker-based Sandboxed Code Executor (Beta) ✨_
+**Text-to-Speech Services**

-<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud Bailian TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS

-_✨ Multimodal Input, Web Search, Text-to-Image ✨_
+## ❤️ Contributing

-<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
+Issues and Pull Requests are always welcome! Feel free to submit your changes to this project :)

-_✨ Natural Language TODO Lists ✨_
+### How to Contribute

-<img src="https://github.com/user-attachments/assets/e137a9e1-340a-4bf2-bb2b-771132780735" height=150>
-<img src="https://github.com/user-attachments/assets/480f5e82-cf6a-4955-a869-0d73137aa6e1" height=150>
+You can contribute by reviewing issues or helping with pull request reviews. Any issues or PRs are welcome to encourage community participation. Of course, these are just suggestions—you can contribute in any way you like. For adding new features, please discuss through an Issue first.

-_✨ Plugin System Showcase ✨_
+### Development Environment

-<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width=600>
+AstrBot uses `ruff` for code formatting and linting.

-_✨ Web Dashboard ✨_
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```

-![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
+## 🌍 Community

-_✨ Built-in Web Chat Interface ✨_
+### QQ Groups

-</div>
+- Group 1: 322154837
+- Group 3: 630166526
+- Group 5: 822130018
+- Group 6: 753075035
+- Developer Group: 975206796
+
+### Telegram Group
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Discord Server
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+## ❤️ Special Thanks
+
+Special thanks to all Contributors and plugin developers for their contributions to AstrBot ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+Additionally, the birth of this project would not have been possible without the help of the following open-source projects:
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - The amazing cat framework

 ## ⭐ Star History

-> [!TIP] 
-> If this project helps you, please give it a star <3
+> [!TIP]
+> If this project has helped you in your life or work, or if you're interested in its future development, please give the project a Star. It's the driving force behind maintaining this open-source project <3

 <div align="center">
-    
-[![Star History Chart](https://api.star-history.com/svg?repos=AstrBotDevs/AstrBot&type=Date)](https://star-history.com/#AstrBotDevs/AstrBot&Date)
+
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)

 </div>

-## Disclaimer
-
-1. Licensed under `AGPL-v3`.
-2. WeChat integration uses [Gewechat](https://github.com/Devo919/Gewechat). Use at your own risk with non-critical accounts.
-3. Users must comply with local laws and regulations.
-
-<!-- ## ✨ ATRI [Beta]
-
-Available as plugin: [astrbot_plugin_atri](https://github.com/AstrBotDevs/AstrBot_plugin_atri)
-
-1. Qwen1.5-7B-Chat Lora model fine-tuned with ATRI character data
-2. Long-term memory
-3. Meme understanding & responses
-4. TTS integration
-    -->
-
+</details>

 _私は、高性能ですから!_
-
@@ -0,0 +1,248 @@
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
+
+</p>
+
+<div align="center">
+
+<br>
+
+<div>
+<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+</div>
+
+<br>
+
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%20plugins&style=for-the-badge&label=Marketplace&cacheSeconds=3600">
+</div>
+
+<br>
+
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>
+
+<a href="https://astrbot.app/">Documentation</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">Feuille de route</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Signaler un problème</a>
+</div>
+
+AstrBot est une plateforme de chatbot Agent tout-en-un open source qui s'intègre aux principales applications de messagerie instantanée. Elle fournit une infrastructure d'IA conversationnelle fiable et évolutive pour les particuliers, les développeurs et les équipes. Que vous construisiez un compagnon IA personnel, un service client intelligent, un assistant d'automatisation ou une base de connaissances d'entreprise, AstrBot vous permet de créer rapidement des applications d'IA prêtes pour la production dans les flux de travail de votre plateforme de messagerie.
+
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />
+
+## Fonctionnalités principales
+
+1. 💯 Gratuit & Open Source.
+2. ✨ Conversations avec LLM IA, Multimodal, Agent, MCP, Base de connaissances, Paramètres de personnalité.
+3. 🤖 Prise en charge de l'intégration avec Dify, Alibaba Cloud Bailian, Coze et autres plateformes d'agents.
+4. 🌐 Multi-plateforme : QQ, WeChat Work, Feishu, DingTalk, Comptes officiels WeChat, Telegram, Slack, et [plus encore](#plateformes-de-messagerie-prises-en-charge).
+5. 📦 Extensions de plugins avec près de 800 plugins disponibles pour une installation en un clic.
+6. 💻 Support WebUI.
+7. 🌐 Support de l'internationalisation (i18n).
+
+## Démarrage rapide
+
+#### Déploiement Docker (Recommandé 🥳)
+
+Nous recommandons de déployer AstrBot en utilisant Docker ou Docker Compose.
+
+Veuillez consulter la documentation officielle : [Déployer AstrBot avec Docker](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot).
+
+#### Déploiement uv
+
+```bash
+uvx astrbot
+```
+
+#### Déploiement BT-Panel
+
+AstrBot s'est associé à BT-Panel et est maintenant disponible sur leur marketplace.
+
+Veuillez consulter la documentation officielle : [Déploiement BT-Panel](https://astrbot.app/deploy/astrbot/btpanel.html).
+
+#### Déploiement 1Panel
+
+AstrBot a été officiellement listé sur le marketplace 1Panel.
+
+Veuillez consulter la documentation officielle : [Déploiement 1Panel](https://astrbot.app/deploy/astrbot/1panel.html).
+
+#### Déployer sur RainYun
+
+AstrBot a été officiellement listé sur la plateforme d'applications cloud de RainYun avec un déploiement en un clic.
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Déployer sur Replit
+
+Méthode de déploiement contribuée par la communauté.
+
+[![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)
+
+#### Installateur Windows en un clic
+
+Veuillez consulter la documentation officielle : [Déployer AstrBot avec l'installateur Windows en un clic](https://astrbot.app/deploy/astrbot/windows.html).
+
+#### Déploiement CasaOS
+
+Méthode de déploiement contribuée par la communauté.
+
+Veuillez consulter la documentation officielle : [Déploiement CasaOS](https://astrbot.app/deploy/astrbot/casaos.html).
+
+#### Déploiement manuel
+
+Tout d'abord, installez uv :
+
+```bash
+pip install uv
+```
+
+Installez AstrBot via Git Clone :
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```
+
+Ou consultez la documentation officielle : [Déployer AstrBot depuis les sources](https://astrbot.app/deploy/astrbot/cli.html).
+
+## Plateformes de messagerie prises en charge
+
+**Maintenues officiellement**
+
+- QQ (Plateforme officielle & OneBot)
+- Telegram
+- Application WeChat Work & Bot intelligent WeChat Work
+- Service client WeChat & Comptes officiels WeChat
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (Bientôt disponible)
+- LINE (Bientôt disponible)
+
+**Maintenues par la communauté**
+
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Messages directs Bilibili](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)
+
+## Services de modèles pris en charge
+
+**Services LLM**
+
+- OpenAI et services compatibles
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- Zhipu AI
+- DeepSeek
+- Ollama (Auto-hébergé)
+- LM Studio (Auto-hébergé)
+- [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [TokenPony](https://www.tokenpony.cn/3YPyf)
+- [SiliconFlow](https://docs.siliconflow.cn/cn/usecases/use-siliconcloud-in-astrbot)
+- [PPIO Cloud](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI
+
+**Plateformes LLMOps**
+
+- Dify
+- Applications Alibaba Cloud Bailian
+- Coze
+
+**Services de reconnaissance vocale**
+
+- OpenAI Whisper
+- SenseVoice
+
+**Services de synthèse vocale**
+
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud Bailian TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS
+
+## ❤️ Contribuer
+
+Les Issues et Pull Requests sont toujours les bienvenues ! N'hésitez pas à soumettre vos modifications à ce projet :)
+
+### Comment contribuer
+
+Vous pouvez contribuer en examinant les issues ou en aidant à la revue des pull requests. Toutes les issues ou PRs sont les bienvenues pour encourager la participation de la communauté. Bien sûr, ce ne sont que des suggestions - vous pouvez contribuer de la manière que vous souhaitez. Pour l'ajout de nouvelles fonctionnalités, veuillez d'abord en discuter via une Issue.
+
+### Environnement de développement
+
+AstrBot utilise `ruff` pour le formatage et le linting du code.
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```
+
+## 🌍 Communauté
+
+### Groupes QQ
+
+- Groupe 1 : 322154837
+- Groupe 3 : 630166526
+- Groupe 5 : 822130018
+- Groupe 6 : 753075035
+- Groupe développeurs : 975206796
+
+### Groupe Telegram
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Serveur Discord
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+## ❤️ Remerciements spéciaux
+
+Un grand merci à tous les contributeurs et développeurs de plugins pour leurs contributions à AstrBot ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+De plus, la naissance de ce projet n'aurait pas été possible sans l'aide des projets open source suivants :
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - L'incroyable framework chat
+
+## ⭐ Historique des étoiles
+
+> [!TIP]
+> Si ce projet vous a aidé dans votre vie ou votre travail, ou si vous êtes intéressé par son développement futur, veuillez donner une étoile au projet. C'est la force motrice derrière la maintenance de ce projet open source <3
+
+<div align="center">
+
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)
+
+</div>
+
+</details>
+
+_私は、高性能ですから!_
+
@@ -1,167 +1,247 @@
-<p align="center">
-
-![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)

 </p>

 <div align="center">

-_✨ 簡単に使えるマルチプラットフォーム LLM チャットボットおよび開発フレームワーク ✨_
+<br>

+<div>
 <a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/AstrBotDevs/AstrBot)](https://github.com/AstrBotDevs/AstrBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
-<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg"/></a>
-<img alt="Static Badge" src="https://img.shields.io/badge/QQ群-630166526-purple">
-[![wakatime](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e.svg)](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e)
-![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7%E6%97%A5%E6%B6%88%E6%81%AF%E4%B8%8A%E8%A1%8C%E9%87%8F&cacheSeconds=3600)
-[![codecov](https://codecov.io/gh/AstrBotDevs/AstrBot/graph/badge.svg?token=FF3P5967B8)](https://codecov.io/gh/AstrBotDevs/AstrBot)
-
-<a href="https://astrbot.app/">ドキュメントを見る</a> ｜
-<a href="https://github.com/AstrBotDevs/AstrBot/issues">問題を報告する</a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 </div>

-AstrBot は、疎結合、非同期、複数のメッセージプラットフォームに対応したデプロイ、使いやすいプラグインシステム、および包括的な大規模言語モデル（LLM）接続機能を備えたチャットボットおよび開発フレームワークです。
+<br>

-## ✨ 主な機能
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E5%80%8B&style=for-the-badge&label=%E3%83%97%E3%83%A9%E3%82%B0%E3%82%A4%E3%83%B3&cacheSeconds=3600">
+</div>

-1. **大規模言語モデルの対話**。OpenAI API、Google Gemini、Llama、Deepseek、ChatGLM など、さまざまな大規模言語モデルをサポートし、Ollama、LLMTuner を介してローカルにデプロイされた大規模モデルをサポートします。多輪対話、人格シナリオ、多モーダル機能を備え、画像理解、音声からテキストへの変換（Whisper）をサポートします。
-2. **複数のメッセージプラットフォームの接続**。QQ（OneBot）、QQ チャンネル、Feishu、Telegram への接続をサポートします。今後、DingTalk、Discord、WhatsApp、Xiaoai 音響をサポートする予定です。レート制限、ホワイトリスト、キーワードフィルタリング、Baidu コンテンツ監査をサポートします。
-3. **エージェント**。一部のエージェント機能をネイティブにサポートし、コードエグゼキューター、自然言語タスク、ウェブ検索などを提供します。[Dify プラットフォーム](https://dify.ai/)と連携し、Dify スマートアシスタント、ナレッジベース、Dify ワークフローを簡単に接続できます。
-4. **プラグインの拡張**。深く最適化されたプラグインメカニズムを備え、[プラグインの開発](https://astrbot.app/dev/plugin.html)をサポートし、機能を拡張できます。複数のプラグインのインストールをサポートします。
-5. **ビジュアル管理パネル**。設定の視覚的な変更、プラグイン管理、ログの表示などをサポートし、設定の難易度を低減します。WebChat を統合し、パネル上で大規模モデルと対話できます。
-6. **高い安定性と高いモジュール性**。イベントバスとパイプラインに基づくアーキテクチャ設計により、高度にモジュール化され、低結合です。
+<br>

-> [!TIP]
-> 管理パネルのオンラインデモを体験する: [https://demo.astrbot.app/](https://demo.astrbot.app/)
->
-> ユーザー名: `astrbot`, パスワード: `astrbot`。LLM が設定されていないため、チャットページで大規模モデルを使用することはできません。（デモのログインパスワードを変更しないでください 😭）
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>

-## ✨ 使用方法
+<a href="https://astrbot.app/">ドキュメント</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">ロードマップ</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue</a>
+</div>

-#### Docker デプロイ
+AstrBot は、主要なインスタントメッセージングアプリと統合できるオープンソースのオールインワン Agent チャットボットプラットフォームです。個人、開発者、チームに信頼性が高くスケーラブルな会話型 AI インフラストラクチャを提供します。パーソナル AI コンパニオン、インテリジェントカスタマーサービス、オートメーションアシスタント、エンタープライズナレッジベースなど、AstrBot を使用すると、IM プラットフォームのワークフロー内で本番環境対応の AI アプリケーションを迅速に構築できます。

-公式ドキュメント [Docker を使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) を参照してください。
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />

-#### Windows ワンクリックインストーラーのデプロイ
+## 主な機能

-コンピュータに Python（>3.10）がインストールされている必要があります。公式ドキュメント [Windows ワンクリックインストーラーを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/windows.html) を参照してください。
+1. 💯 無料 & オープンソース。
+2. ✨ AI 大規模言語モデル対話、マルチモーダル、Agent、MCP、ナレッジベース、ペルソナ設定。
+3. 🤖 Dify、Alibaba Cloud 百炼、Coze などの Agent プラットフォームとの統合をサポート。
+4. 🌐 マルチプラットフォーム：QQ、WeChat Work、Feishu、DingTalk、WeChat 公式アカウント、Telegram、Slack、[その他](#サポートされているメッセージプラットフォーム)。
+5. 📦 約800個のプラグインをワンクリックでインストール可能なプラグイン拡張機能。
+6. 💻 WebUI サポート。
+7. 🌐 国際化（i18n）サポート。

-#### Replit デプロイ
+## クイックスタート
+
+#### Docker デプロイ（推奨 🥳）
+
+Docker / Docker Compose を使用した AstrBot のデプロイを推奨します。
+
+公式ドキュメント [Docker を使用した AstrBot のデプロイ](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) をご参照ください。
+
+#### uv デプロイ
+
+```bash
+uvx astrbot
+```
+
+#### 宝塔パネルデプロイ
+
+AstrBot は宝塔パネルと提携し、宝塔パネルに公開されています。
+
+公式ドキュメント [宝塔パネルデプロイ](https://astrbot.app/deploy/astrbot/btpanel.html) をご参照ください。
+
+#### 1Panel デプロイ
+
+AstrBot は 1Panel 公式により 1Panel パネルに公開されています。
+
+公式ドキュメント [1Panel デプロイ](https://astrbot.app/deploy/astrbot/1panel.html) をご参照ください。
+
+#### 雨云でのデプロイ
+
+AstrBot は雨云公式によりクラウドアプリケーションプラットフォームに公開され、ワンクリックでデプロイ可能です。
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Replit でのデプロイ
+
+コミュニティ貢献によるデプロイ方法。

 [![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)

+#### Windows ワンクリックインストーラーデプロイ
+
+公式ドキュメント [Windows ワンクリックインストーラーを使用した AstrBot のデプロイ](https://astrbot.app/deploy/astrbot/windows.html) をご参照ください。
+
 #### CasaOS デプロイ

-コミュニティが提供するデプロイ方法です。
+コミュニティ貢献によるデプロイ方法。

-公式ドキュメント [ソースコードを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/casaos.html) を参照してください。
+公式ドキュメント [CasaOS デプロイ](https://astrbot.app/deploy/astrbot/casaos.html) をご参照ください。

 #### 手動デプロイ

-公式ドキュメント [ソースコードを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/cli.html) を参照してください。
+まず uv をインストールします:

-## ⚡ メッセージプラットフォームのサポート状況
+```bash
+pip install uv
+```

-| プラットフォーム    | サポート状況 | 詳細 | メッセージタイプ |
-| -------- | ------- | ------- | ------ |
-| QQ(公式ロボットインターフェース) | ✔    | プライベートチャット、グループチャット、QQ チャンネルプライベートチャット、グループチャット | テキスト、画像 |
-| QQ(OneBot)      | ✔    | プライベートチャット、グループチャット | テキスト、画像、音声 |
-| WeChat(個人アカウント)    | ✔    | WeChat 個人アカウントのプライベートチャット、グループチャット | テキスト、画像、音声 |
-| [Telegram](https://github.com/Soulter/astrbot_plugin_telegram)   | ✔    | プライベートチャット、グループチャット | テキスト、画像 |
-| [WeChat(企業 WeChat)](https://github.com/Soulter/astrbot_plugin_wecom)    | ✔    | プライベートチャット | テキスト、画像、音声 |
-| Feishu   | ✔    | グループチャット | テキスト、画像 |
-| WeChat 対話オープンプラットフォーム | 🚧    | 計画中 | - |
-| Discord   | 🚧    | 計画中 | - |
-| WhatsApp   | 🚧    | 計画中 | - |
-| Xiaoai 音響   | 🚧    | 計画中 | - |
+Git Clone で AstrBot をインストール:

-# 🦌 今後のロードマップ
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```

-> [!TIP]
-> Issue でさらに多くの提案を歓迎します <3
+または、公式ドキュメント [ソースコードから AstrBot をデプロイ](https://astrbot.app/deploy/astrbot/cli.html) をご参照ください。

- [ ] 現在のすべてのプラットフォームアダプターの機能の一貫性を確保し、改善する
- [ ] プラグインインターフェースの最適化
- [ ] GPT-Sovits などの TTS サービスをデフォルトでサポート
- [ ] "チャット強化" 部分を完成させ、永続的な記憶をサポート
- [ ] i18n の計画
+## サポートされているメッセージプラットフォーム

-## ❤️ 貢献
+**公式メンテナンス**

-Issue や Pull Request を歓迎します！このプロジェクトに変更を加えるだけです :)
+- QQ (公式プラットフォーム & OneBot)
+- Telegram
+- WeChat Work アプリケーション & WeChat Work インテリジェントボット
+- WeChat カスタマーサービス & WeChat 公式アカウント
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (近日対応予定)
+- LINE (近日対応予定)

-新機能の追加については、まず Issue で議論してください。
+**コミュニティメンテナンス**

-## 🌟 サポート
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili ダイレクトメッセージ](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)

- このプロジェクトに Star を付けてください！
- [愛発電](https://afdian.com/a/soulter)で私をサポートしてください！
- [WeChat](https://drive.soulter.top/f/pYfA/d903f4fa49a496fda3f16d2be9e023b5.png)で私をサポートしてください~
+## サポートされているモデルサービス

-## ✨ デモ
+**大規模言語モデルサービス**

-> [!NOTE]
-> コードエグゼキューターのファイル入力/出力は現在 Napcat(QQ)、Lagrange(QQ) でのみテストされています
+- OpenAI および互換サービス
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- 智谱 AI
+- DeepSeek
+- Ollama (セルフホスト)
+- LM Studio (セルフホスト)
+- [優云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [小馬算力](https://www.tokenpony.cn/3YPyf)
+- [硅基流動](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
+- [PPIO 派欧云](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI

-<div align='center'>
+**LLMOps プラットフォーム**

-<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
+- Dify
+- Alibaba Cloud 百炼アプリケーション
+- Coze

-_✨ Docker ベースのサンドボックス化されたコードエグゼキューター（ベータテスト中）✨_
+**音声認識サービス**

-<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
+- OpenAI Whisper
+- SenseVoice

-_✨ 多モーダル、ウェブ検索、長文の画像変換（設定可能）✨_
+**音声合成サービス**

-<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud 百炼 TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS

-_✨ 自然言語タスク ✨_
+## ❤️ コントリビューション

-<img src="https://github.com/user-attachments/assets/e137a9e1-340a-4bf2-bb2b-771132780735" height=150>
-<img src="https://github.com/user-attachments/assets/480f5e82-cf6a-4955-a869-0d73137aa6e1" height=150>
+Issue や Pull Request は大歓迎です!このプロジェクトに変更を送信してください :)

-_✨ プラグインシステム - 一部のプラグインの展示 ✨_
+### コントリビュート方法

-<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width="600">
+Issue を確認したり、PR(プルリクエスト)のレビューを手伝うことで貢献できます。どんな Issue や PR への参加も歓迎され、コミュニティ貢献を促進します。もちろん、これらは提案に過ぎず、どんな方法でも貢献できます。新機能の追加については、まず Issue で議論してください。

-_✨ 管理パネル ✨_
+### 開発環境

-![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
+AstrBot はコードのフォーマットとチェックに `ruff` を使用しています。

-_✨ 内蔵 Web Chat、オンラインでボットと対話 ✨_
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```

-</div>
+## 🌍 コミュニティ
+
+### QQ グループ
+
+- 1群: 322154837
+- 3群: 630166526
+- 5群: 822130018
+- 6群: 753075035
+- 開発者群: 975206796
+
+### Telegram グループ
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Discord サーバー
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+## ❤️ Special Thanks
+
+AstrBot への貢献をしていただいたすべてのコントリビューターとプラグイン開発者に特別な感謝を ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+また、このプロジェクトの誕生は以下のオープンソースプロジェクトの助けなしには実現できませんでした:
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - 素晴らしい猫猫フレームワーク

 ## ⭐ Star History

 > [!TIP]
-> このプロジェクトがあなたの生活や仕事に役立った場合、またはこのプロジェクトの将来の発展に関心がある場合は、プロジェクトに Star を付けてください。これはこのオープンソースプロジェクトを維持するためのモチベーションです <3
+> このプロジェクトがあなたの生活や仕事に役立ったり、このプロジェクトの今後の発展に関心がある場合は、プロジェクトに Star をください。これがこのオープンソースプロジェクトを維持する原動力です <3

 <div align="center">

-[![Star History Chart](https://api.star-history.com/svg?repos=soulter/astrbot&type=Date)](https://star-history.com/#soulter/astrbot&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)

 </div>

-## スポンサー
-
-[<img src="https://api.gitsponsors.com/api/badge/img?id=575865240" height="20">](https://api.gitsponsors.com/api/badge/link?p=XEpbdGxlitw/RbcwiTX93UMzNK/jgDYC8NiSzamIPMoKvG2lBFmyXhSS/b0hFoWlBBMX2L5X5CxTDsUdyvcIEHTOfnkXz47UNOZvMwyt5CzbYpq0SEzsSV1OJF1cCo90qC/ZyYKYOWedal3MhZ3ikw==)
-
-## 免責事項
-
-1. このプロジェクトは `AGPL-v3` オープンソースライセンスの下で保護されています。
-2. このプロジェクトを使用する際は、現地の法律および規制を遵守してください。
-
-<!-- ## ✨ ATRI [ベータテスト]
-
-この機能はプラグインとしてロードされます。プラグインリポジトリのアドレス：[astrbot_plugin_atri](https://github.com/Soulter/astrbot_plugin_atri)
-
-1. 《ATRI ~ My Dear Moments》の主人公 ATRI のキャラクターセリフを微調整データセットとして使用した `Qwen1.5-7B-Chat Lora` 微調整モデル。
-2. 長期記憶
-3. ミームの理解と返信
-4. TTS
-    -->
+</details>

 _私は、高性能ですから!_
@@ -0,0 +1,248 @@
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
+
+</p>
+
+<div align="center">
+
+<br>
+
+<div>
+<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+</div>
+
+<br>
+
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%20%D0%BF%D0%BB%D0%B0%D0%B3%D0%B8%D0%BD%D0%BE%D0%B2&style=for-the-badge&label=%D0%9C%D0%B0%D0%B3%D0%B0%D0%B7%D0%B8%D0%BD&cacheSeconds=3600">
+</div>
+
+<br>
+
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_zh-TW.md">繁體中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a>
+
+<a href="https://astrbot.app/">Документация</a> ｜
+<a href="https://blog.astrbot.app/">Блог</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">Дорожная карта</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Сообщить о проблеме</a>
+</div>
+
+AstrBot — это универсальная платформа Agent-чатботов с открытым исходным кодом, которая интегрируется с основными приложениями для обмена мгновенными сообщениями. Она предоставляет надёжную и масштабируемую инфраструктуру разговорного ИИ для частных лиц, разработчиков и команд. Будь то персональный ИИ-компаньон, интеллектуальная служба поддержки, автоматизированный помощник или корпоративная база знаний — AstrBot позволяет быстро создавать готовые к использованию ИИ-приложения в рабочих процессах вашей платформы обмена сообщениями.
+
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />
+
+## Основные возможности
+
+1. 💯 Бесплатно и с открытым исходным кодом.
+2. ✨ ИИ-диалоги с LLM, мультимодальность, Agent, MCP, база знаний, настройки личности.
+3. 🤖 Поддержка интеграции с Dify, Alibaba Cloud Bailian, Coze и другими платформами агентов.
+4. 🌐 Мультиплатформенность: QQ, WeChat Work, Feishu, DingTalk, официальные аккаунты WeChat, Telegram, Slack и [другие](#поддерживаемые-платформы-обмена-сообщениями).
+5. 📦 Расширения плагинов с почти 800 плагинами, доступными для установки в один клик.
+6. 💻 Поддержка WebUI.
+7. 🌐 Поддержка интернационализации (i18n).
+
+## Быстрый старт
+
+#### Развёртывание Docker (Рекомендуется 🥳)
+
+Мы рекомендуем развёртывать AstrBot с помощью Docker или Docker Compose.
+
+См. официальную документацию: [Развёртывание AstrBot с Docker](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot).
+
+#### Развёртывание uv
+
+```bash
+uvx astrbot
+```
+
+#### Развёртывание BT-Panel
+
+AstrBot в партнёрстве с BT-Panel теперь доступен на их маркетплейсе.
+
+См. официальную документацию: [Развёртывание BT-Panel](https://astrbot.app/deploy/astrbot/btpanel.html).
+
+#### Развёртывание 1Panel
+
+AstrBot официально размещён на маркетплейсе 1Panel.
+
+См. официальную документацию: [Развёртывание 1Panel](https://astrbot.app/deploy/astrbot/1panel.html).
+
+#### Развёртывание на RainYun
+
+AstrBot официально размещён на облачной платформе приложений RainYun с развёртыванием в один клик.
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Развёртывание на Replit
+
+Метод развёртывания от сообщества.
+
+[![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)
+
+#### Установщик Windows в один клик
+
+См. официальную документацию: [Развёртывание AstrBot с установщиком Windows в один клик](https://astrbot.app/deploy/astrbot/windows.html).
+
+#### Развёртывание CasaOS
+
+Метод развёртывания от сообщества.
+
+См. официальную документацию: [Развёртывание CasaOS](https://astrbot.app/deploy/astrbot/casaos.html).
+
+#### Ручное развёртывание
+
+Сначала установите uv:
+
+```bash
+pip install uv
+```
+
+Установите AstrBot через Git Clone:
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```
+
+Или см. официальную документацию: [Развёртывание AstrBot из исходного кода](https://astrbot.app/deploy/astrbot/cli.html).
+
+## Поддерживаемые платформы обмена сообщениями
+
+**Официально поддерживаемые**
+
+- QQ (Официальная платформа и OneBot)
+- Telegram
+- Приложение WeChat Work и интеллектуальный бот WeChat Work
+- Служба поддержки WeChat и официальные аккаунты WeChat
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (Скоро)
+- LINE (Скоро)
+
+**Поддерживаемые сообществом**
+
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Личные сообщения Bilibili](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)
+
+## Поддерживаемые сервисы моделей
+
+**Сервисы LLM**
+
+- OpenAI и совместимые сервисы
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- Zhipu AI
+- DeepSeek
+- Ollama (Самостоятельное размещение)
+- LM Studio (Самостоятельное размещение)
+- [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [TokenPony](https://www.tokenpony.cn/3YPyf)
+- [SiliconFlow](https://docs.siliconflow.cn/cn/usecases/use-siliconcloud-in-astrbot)
+- [PPIO Cloud](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI
+
+**Платформы LLMOps**
+
+- Dify
+- Приложения Alibaba Cloud Bailian
+- Coze
+
+**Сервисы распознавания речи**
+
+- OpenAI Whisper
+- SenseVoice
+
+**Сервисы синтеза речи**
+
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud Bailian TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS
+
+## ❤️ Вклад в проект
+
+Issues и Pull Request всегда приветствуются! Не стесняйтесь отправлять свои изменения в этот проект :)
+
+### Как внести вклад
+
+Вы можете внести вклад, просматривая issues или помогая с ревью pull request. Любые issues или PR приветствуются для поощрения участия сообщества. Конечно, это лишь предложения — вы можете вносить вклад любым удобным для вас способом. Для добавления новых функций сначала обсудите это через Issue.
+
+### Среда разработки
+
+AstrBot использует `ruff` для форматирования и линтинга кода.
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```
+
+## 🌍 Сообщество
+
+### Группы QQ
+
+- Группа 1: 322154837
+- Группа 3: 630166526
+- Группа 5: 822130018
+- Группа 6: 753075035
+- Группа разработчиков: 975206796
+
+### Группа Telegram
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Сервер Discord
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+## ❤️ Особая благодарность
+
+Особая благодарность всем контрибьюторам и разработчикам плагинов за их вклад в AstrBot ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+Кроме того, рождение этого проекта было бы невозможно без помощи следующих проектов с открытым исходным кодом:
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - Замечательный кошачий фреймворк
+
+## ⭐ История звёзд
+
+> [!TIP]
+> Если этот проект помог вам в жизни или работе, или если вас интересует его будущее развитие, пожалуйста, поставьте проекту звезду. Это движущая сила поддержки этого проекта с открытым исходным кодом <3
+
+<div align="center">
+
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)
+
+</div>
+
+</details>
+
+_私は、高性能ですから!_
+
@@ -0,0 +1,248 @@
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
+
+</p>
+
+<div align="center">
+
+<br>
+
+<div>
+<a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+</div>
+
+<br>
+
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E5%80%8B&style=for-the-badge&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%A0%B4&cacheSeconds=3600">
+</div>
+
+<br>
+
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">简体中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_fr.md">Français</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ru.md">Русский</a>
+
+<a href="https://astrbot.app/">文件</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">路線圖</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">問題回報</a>
+</div>
+
+AstrBot 是一個開源的一站式 Agent 聊天機器人平台，可接入主流即時通訊軟體，為個人、開發者和團隊打造可靠、可擴展的對話式智慧基礎設施。無論是個人 AI 夥伴、智慧客服、自動化助手，還是企業知識庫，AstrBot 都能在您的即時通訊軟體平台的工作流程中快速構建生產可用的 AI 應用程式。
+
+<img width="1776" height="1080" alt="image" src="https://github.com/user-attachments/assets/00782c4c-4437-4d97-aabc-605e3738da5c" />
+
+## 主要功能
+
+1. 💯 免費 & 開源。
+2. ✨ AI 大型模型對話，多模態，Agent，MCP，知識庫，人格設定。
+3. 🤖 支援接入 Dify、阿里雲百煉、Coze 等智慧體平台。
+4. 🌐 多平台：QQ、企業微信、飛書、釘釘、微信公眾號、Telegram、Slack 以及[更多](#支援的訊息平台)。
+5. 📦 外掛擴充，已有近 800 個外掛可一鍵安裝。
+6. 💻 WebUI 支援。
+7. 🌐 國際化（i18n）支援。
+
+## 快速開始
+
+#### Docker 部署（推薦 🥳）
+
+推薦使用 Docker / Docker Compose 方式部署 AstrBot。
+
+請參閱官方文件 [使用 Docker 部署 AstrBot](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot)。
+
+#### uv 部署
+
+```bash
+uvx astrbot
+```
+
+#### 寶塔面板部署
+
+AstrBot 與寶塔面板合作，已上架至寶塔面板。
+
+請參閱官方文件 [寶塔面板部署](https://astrbot.app/deploy/astrbot/btpanel.html)。
+
+#### 1Panel 部署
+
+AstrBot 已由 1Panel 官方上架至 1Panel 面板。
+
+請參閱官方文件 [1Panel 部署](https://astrbot.app/deploy/astrbot/1panel.html)。
+
+#### 在雨雲上部署
+
+AstrBot 已由雨雲官方上架至雲端應用程式平台，可一鍵部署。
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### 在 Replit 上部署
+
+社群貢獻的部署方式。
+
+[![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)
+
+#### Windows 一鍵安裝器部署
+
+請參閱官方文件 [使用 Windows 一鍵安裝器部署 AstrBot](https://astrbot.app/deploy/astrbot/windows.html)。
+
+#### CasaOS 部署
+
+社群貢獻的部署方式。
+
+請參閱官方文件 [CasaOS 部署](https://astrbot.app/deploy/astrbot/casaos.html)。
+
+#### 手動部署
+
+首先安裝 uv：
+
+```bash
+pip install uv
+```
+
+透過 Git Clone 安裝 AstrBot：
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```
+
+或者請參閱官方文件 [透過原始碼部署 AstrBot](https://astrbot.app/deploy/astrbot/cli.html)。
+
+## 支援的訊息平台
+
+**官方維護**
+
+- QQ（官方平台 & OneBot）
+- Telegram
+- 企微應用 & 企微智慧機器人
+- 微信客服 & 微信公眾號
+- 飛書
+- 釘釘
+- Slack
+- Discord
+- Satori
+- Misskey
+- Whatsapp（即將支援）
+- LINE（即將支援）
+
+**社群維護**
+
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili 私訊](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)
+
+## 支援的模型服務
+
+**大型模型服務**
+
+- OpenAI 及相容服務
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- 智譜 AI
+- DeepSeek
+- Ollama（本機部署）
+- LM Studio（本機部署）
+- [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [小馬算力](https://www.tokenpony.cn/3YPyf)
+- [矽基流動](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
+- [PPIO 派歐雲](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI
+
+**LLMOps 平台**
+
+- Dify
+- 阿里雲百煉應用
+- Coze
+
+**語音轉文字服務**
+
+- OpenAI Whisper
+- SenseVoice
+
+**文字轉語音服務**
+
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- 阿里雲百煉 TTS
+- Azure TTS
+- Minimax TTS
+- 火山引擎 TTS
+
+## ❤️ 貢獻
+
+歡迎任何 Issues/Pull Requests！只需要將您的變更提交到此專案 ：)
+
+### 如何貢獻
+
+您可以透過檢視問題或協助審核 PR（拉取請求）來貢獻。任何問題或 PR 都歡迎參與，以促進社群貢獻。當然，這些只是建議，您可以以任何方式進行貢獻。對於新功能的新增，請先透過 Issue 討論。
+
+### 開發環境
+
+AstrBot 使用 `ruff` 進行程式碼格式化和檢查。
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```
+
+## 🌍 社群
+
+### QQ 群組
+
+- 1 群：322154837
+- 3 群：630166526
+- 5 群：822130018
+- 6 群：753075035
+- 開發者群：975206796
+
+### Telegram 群組
+
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+### Discord 群組
+
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+
+## ❤️ Special Thanks
+
+特別感謝所有 Contributors 和外掛開發者對 AstrBot 的貢獻 ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+此外，本專案的誕生離不開以下開源專案的幫助：
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - 偉大的貓貓框架
+
+## ⭐ Star History
+
+> [!TIP]
+> 如果本專案對您的生活 / 工作產生了幫助，或者您關注本專案的未來發展，請給專案 Star，這是我們維護這個開源專案的動力 <3
+
+<div align="center">
+
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)
+
+</div>
+
+</details>
+
+_私は、高性能ですから!_
+
@@ -36,7 +36,8 @@ from astrbot.core.star.config import *


 # provider
-from astrbot.core.provider import Provider, Personality, ProviderMetaData
+from astrbot.core.provider import Provider, ProviderMetaData
+from astrbot.core.db.po import Personality

 # platform
 from astrbot.core.platform import (
@@ -1,4 +1,5 @@
-from astrbot.core.provider import Personality, Provider, STTProvider
+from astrbot.core.db.po import Personality
+from astrbot.core.provider import Provider, STTProvider
 from astrbot.core.provider.entities import (
    LLMResponse,
    ProviderMetaData,
@@ -1 +1 @@
-__version__ = "3.5.23"
+__version__ = "4.9.1"
@@ -4,6 +4,14 @@ from contextlib import AsyncExitStack
 from datetime import timedelta
 from typing import Generic

+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+
 from astrbot import logger
 from astrbot.core.agent.run_context import ContextWrapper
 from astrbot.core.utils.log_pipe import LogPipe
@@ -12,21 +20,24 @@ from .run_context import TContext
 from .tool import FunctionTool

 try:
+    import anyio
    import mcp
    from mcp.client.sse import sse_client
 except (ModuleNotFoundError, ImportError):
-    logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
+    logger.warning(
+        "Warning: Missing 'mcp' dependency, MCP services will be unavailable."
+    )

 try:
    from mcp.client.streamable_http import streamablehttp_client
 except (ModuleNotFoundError, ImportError):
    logger.warning(
-        "警告: 缺少依赖库 'mcp' 或者 mcp 库版本过低，无法使用 Streamable HTTP 连接方式。",
+        "Warning: Missing 'mcp' dependency or MCP library version too old, Streamable HTTP connection unavailable.",
    )


 def _prepare_config(config: dict) -> dict:
-    """准备配置，处理嵌套格式"""
+    """Prepare configuration, handle nested format"""
    if config.get("mcpServers"):
        first_key = next(iter(config["mcpServers"]))
        config = config["mcpServers"][first_key]
@@ -35,7 +46,7 @@ def _prepare_config(config: dict) -> dict:


 async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
-    """快速测试 MCP 服务器可达性"""
+    """Quick test MCP server connectivity"""
    import aiohttp

    cfg = _prepare_config(config.copy())
@@ -50,7 +61,7 @@ async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
        elif "type" in cfg:
            transport_type = cfg["type"]
        else:
-            raise Exception("MCP 连接配置缺少 transport 或 type 字段")
+            raise Exception("MCP connection config missing transport or type field")

        async with aiohttp.ClientSession() as session:
            if transport_type == "streamable_http":
@@ -91,7 +102,7 @@ async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
                    return False, f"HTTP {response.status}: {response.reason}"

    except asyncio.TimeoutError:
-        return False, f"连接超时: {timeout}秒"
+        return False, f"Connection timeout: {timeout} seconds"
    except Exception as e:
        return False, f"{e!s}"

@@ -101,6 +112,7 @@ class MCPClient:
        # Initialize session and client objects
        self.session: mcp.ClientSession | None = None
        self.exit_stack = AsyncExitStack()
+        self._old_exit_stacks: list[AsyncExitStack] = []  # Track old stacks for cleanup

        self.name: str | None = None
        self.active: bool = True
@@ -108,22 +120,32 @@ class MCPClient:
        self.server_errlogs: list[str] = []
        self.running_event = asyncio.Event()

-    async def connect_to_server(self, mcp_server_config: dict, name: str):
-        """连接到 MCP 服务器
+        # Store connection config for reconnection
+        self._mcp_server_config: dict | None = None
+        self._server_name: str | None = None
+        self._reconnect_lock = asyncio.Lock()  # Lock for thread-safe reconnection
+        self._reconnecting: bool = False  # For logging and debugging

-        如果 `url` 参数存在：
-            1. 当 transport 指定为 `streamable_http` 时，使用 Streamable HTTP 连接方式。
-            1. 当 transport 指定为 `sse` 时，使用 SSE 连接方式。
-            2. 如果没有指定，默认使用 SSE 的方式连接到 MCP 服务。
+    async def connect_to_server(self, mcp_server_config: dict, name: str):
+        """Connect to MCP server
+
+        If `url` parameter exists:
+            1. When transport is specified as `streamable_http`, use Streamable HTTP connection.
+            2. When transport is specified as `sse`, use SSE connection.
+            3. If not specified, default to SSE connection to MCP service.

        Args:
            mcp_server_config (dict): Configuration for the MCP server. See https://modelcontextprotocol.io/quickstart/server

        """
+        # Store config for reconnection
+        self._mcp_server_config = mcp_server_config
+        self._server_name = name
+
        cfg = _prepare_config(mcp_server_config.copy())

        def logging_callback(msg: str):
-            # 处理 MCP 服务的错误日志
+            # Handle MCP service error logs
            print(f"MCP Server {name} Error: {msg}")
            self.server_errlogs.append(msg)

@@ -137,7 +159,7 @@ class MCPClient:
            elif "type" in cfg:
                transport_type = cfg["type"]
            else:
-                raise Exception("MCP 连接配置缺少 transport 或 type 字段")
+                raise Exception("MCP connection config missing transport or type field")

            if transport_type != "streamable_http":
                # SSE transport method
@@ -193,7 +215,7 @@ class MCPClient:
            )

            def callback(msg: str):
-                # 处理 MCP 服务的错误日志
+                # Handle MCP service error logs
                self.server_errlogs.append(msg)

            stdio_transport = await self.exit_stack.enter_async_context(
@@ -222,10 +244,120 @@ class MCPClient:
        self.tools = response.tools
        return response

+    async def _reconnect(self) -> None:
+        """Reconnect to the MCP server using the stored configuration.
+
+        Uses asyncio.Lock to ensure thread-safe reconnection in concurrent environments.
+
+        Raises:
+            Exception: raised when reconnection fails
+        """
+        async with self._reconnect_lock:
+            # Check if already reconnecting (useful for logging)
+            if self._reconnecting:
+                logger.debug(
+                    f"MCP Client {self._server_name} is already reconnecting, skipping"
+                )
+                return
+
+            if not self._mcp_server_config or not self._server_name:
+                raise Exception("Cannot reconnect: missing connection configuration")
+
+            self._reconnecting = True
+            try:
+                logger.info(
+                    f"Attempting to reconnect to MCP server {self._server_name}..."
+                )
+
+                # Save old exit_stack for later cleanup (don't close it now to avoid cancel scope issues)
+                if self.exit_stack:
+                    self._old_exit_stacks.append(self.exit_stack)
+
+                # Mark old session as invalid
+                self.session = None
+
+                # Create new exit stack for new connection
+                self.exit_stack = AsyncExitStack()
+
+                # Reconnect using stored config
+                await self.connect_to_server(self._mcp_server_config, self._server_name)
+                await self.list_tools_and_save()
+
+                logger.info(
+                    f"Successfully reconnected to MCP server {self._server_name}"
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to reconnect to MCP server {self._server_name}: {e}"
+                )
+                raise
+            finally:
+                self._reconnecting = False
+
+    async def call_tool_with_reconnect(
+        self,
+        tool_name: str,
+        arguments: dict,
+        read_timeout_seconds: timedelta,
+    ) -> mcp.types.CallToolResult:
+        """Call MCP tool with automatic reconnection on failure, max 2 retries.
+
+        Args:
+            tool_name: tool name
+            arguments: tool arguments
+            read_timeout_seconds: read timeout
+
+        Returns:
+            MCP tool call result
+
+        Raises:
+            ValueError: MCP session is not available
+            anyio.ClosedResourceError: raised after reconnection failure
+        """
+
+        @retry(
+            retry=retry_if_exception_type(anyio.ClosedResourceError),
+            stop=stop_after_attempt(2),
+            wait=wait_exponential(multiplier=1, min=1, max=3),
+            before_sleep=before_sleep_log(logger, logging.WARNING),
+            reraise=True,
+        )
+        async def _call_with_retry():
+            if not self.session:
+                raise ValueError("MCP session is not available for MCP function tools.")
+
+            try:
+                return await self.session.call_tool(
+                    name=tool_name,
+                    arguments=arguments,
+                    read_timeout_seconds=read_timeout_seconds,
+                )
+            except anyio.ClosedResourceError:
+                logger.warning(
+                    f"MCP tool {tool_name} call failed (ClosedResourceError), attempting to reconnect..."
+                )
+                # Attempt to reconnect
+                await self._reconnect()
+                # Reraise the exception to trigger tenacity retry
+                raise
+
+        return await _call_with_retry()
+
    async def cleanup(self):
-        """Clean up resources"""
-        await self.exit_stack.aclose()
-        self.running_event.set()  # Set the running event to indicate cleanup is done
+        """Clean up resources including old exit stacks from reconnections"""
+        # Close current exit stack
+        try:
+            await self.exit_stack.aclose()
+        except Exception as e:
+            logger.debug(f"Error closing current exit stack: {e}")
+
+        # Don't close old exit stacks as they may be in different task contexts
+        # They will be garbage collected naturally
+        # Just clear the list to release references
+        self._old_exit_stacks.clear()
+
+        # Set running_event first to unblock any waiting tasks
+        self.running_event.set()


 class MCPTool(FunctionTool, Generic[TContext]):
@@ -246,14 +378,8 @@ class MCPTool(FunctionTool, Generic[TContext]):
    async def call(
        self, context: ContextWrapper[TContext], **kwargs
    ) -> mcp.types.CallToolResult:
-        session = self.mcp_client.session
-        if not session:
-            raise ValueError("MCP session is not available for MCP function tools.")
-        res = await session.call_tool(
-            name=self.mcp_tool.name,
+        return await self.mcp_client.call_tool_with_reconnect(
+            tool_name=self.mcp_tool.name,
            arguments=kwargs,
-            read_timeout_seconds=timedelta(
-                seconds=context.tool_call_timeout,
-            ),
+            read_timeout_seconds=timedelta(seconds=context.tool_call_timeout),
        )
-        return res
@@ -3,7 +3,7 @@

 from typing import Any, ClassVar, Literal, cast

-from pydantic import BaseModel, GetCoreSchemaHandler
+from pydantic import BaseModel, GetCoreSchemaHandler, model_validator
 from pydantic_core import core_schema


@@ -76,7 +76,7 @@ class ImageURLPart(ContentPart):
        """The ID of the image, to allow LLMs to distinguish different images."""

    type: str = "image_url"
-    image_url: str
+    image_url: ImageURL


 class AudioURLPart(ContentPart):
@@ -119,6 +119,13 @@ class ToolCall(BaseModel):
    """The ID of the tool call."""
    function: FunctionBody
    """The function body of the tool call."""
+    extra_content: dict[str, Any] | None = None
+    """Extra metadata for the tool call."""
+
+    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
+        if self.extra_content is None:
+            kwargs.setdefault("exclude", set()).add("extra_content")
+        return super().model_dump(**kwargs)


 class ToolCallPart(BaseModel):
@@ -138,22 +145,39 @@ class Message(BaseModel):
        "tool",
    ]

-    content: str | list[ContentPart]
+    content: str | list[ContentPart] | None = None
    """The content of the message."""

+    tool_calls: list[ToolCall] | list[dict] | None = None
+    """The tool calls of the message."""
+
+    tool_call_id: str | None = None
+    """The ID of the tool call."""
+
+    @model_validator(mode="after")
+    def check_content_required(self):
+        # assistant + tool_calls is not None: allow content to be None
+        if self.role == "assistant" and self.tool_calls is not None:
+            return self
+
+        # other all cases: content is required
+        if self.content is None:
+            raise ValueError(
+                "content is required unless role='assistant' and tool_calls is not None"
+            )
+        return self
+

 class AssistantMessageSegment(Message):
    """A message segment from the assistant."""

    role: Literal["assistant"] = "assistant"
-    tool_calls: list[ToolCall] | list[dict] | None = None


 class ToolCallMessageSegment(Message):
    """A message segment representing a tool call."""

    role: Literal["tool"] = "tool"
-    tool_call_id: str


 class UserMessageSegment(Message):
@@ -1,16 +1,21 @@
-from dataclasses import dataclass
 from typing import Any, Generic

+from pydantic import Field
+from pydantic.dataclasses import dataclass
 from typing_extensions import TypeVar

+from .message import Message
+
 TContext = TypeVar("TContext", default=Any)


-@dataclass
+@dataclass(config={"arbitrary_types_allowed": True})
 class ContextWrapper(Generic[TContext]):
    """A context for running an agent, which can be used to pass additional data or state."""

    context: TContext
+    messages: list[Message] = Field(default_factory=list)
+    """This field stores the llm message context for the agent run, agent runners will maintain this field automatically."""
    tool_call_timeout: int = 60  # Default tool call timeout in seconds


@@ -2,13 +2,12 @@ import abc
 import typing as T
 from enum import Enum, auto

-from astrbot.core.provider import Provider
+from astrbot import logger
 from astrbot.core.provider.entities import LLMResponse

 from ..hooks import BaseAgentRunHooks
 from ..response import AgentResponse
 from ..run_context import ContextWrapper, TContext
-from ..tool_executor import BaseFunctionToolExecutor


 class AgentState(Enum):
@@ -24,9 +23,7 @@ class BaseAgentRunner(T.Generic[TContext]):
    @abc.abstractmethod
    async def reset(
        self,
-        provider: Provider,
        run_context: ContextWrapper[TContext],
-        tool_executor: BaseFunctionToolExecutor[TContext],
        agent_hooks: BaseAgentRunHooks[TContext],
        **kwargs: T.Any,
    ) -> None:
@@ -40,6 +37,13 @@ class BaseAgentRunner(T.Generic[TContext]):
        """Process a single step of the agent."""
        ...

+    @abc.abstractmethod
+    async def step_until_done(
+        self, max_step: int
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        """Process steps until the agent is done."""
+        ...
+
    @abc.abstractmethod
    def done(self) -> bool:
        """Check if the agent has completed its task.
@@ -53,3 +57,9 @@ class BaseAgentRunner(T.Generic[TContext]):
        This method should be called after the agent is done.
        """
        ...
+
+    def _transition_state(self, new_state: AgentState) -> None:
+        """Transition the agent state."""
+        if self._state != new_state:
+            logger.debug(f"Agent state transition: {self._state} -> {new_state}")
+            self._state = new_state
@@ -0,0 +1,367 @@
+import base64
+import json
+import sys
+import typing as T
+
+import astrbot.core.message.components as Comp
+from astrbot import logger
+from astrbot.core import sp
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import (
+    LLMResponse,
+    ProviderRequest,
+)
+
+from ...hooks import BaseAgentRunHooks
+from ...response import AgentResponseData
+from ...run_context import ContextWrapper, TContext
+from ..base import AgentResponse, AgentState, BaseAgentRunner
+from .coze_api_client import CozeAPIClient
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
+
+class CozeAgentRunner(BaseAgentRunner[TContext]):
+    """Coze Agent Runner"""
+
+    @override
+    async def reset(
+        self,
+        request: ProviderRequest,
+        run_context: ContextWrapper[TContext],
+        agent_hooks: BaseAgentRunHooks[TContext],
+        provider_config: dict,
+        **kwargs: T.Any,
+    ) -> None:
+        self.req = request
+        self.streaming = kwargs.get("streaming", False)
+        self.final_llm_resp = None
+        self._state = AgentState.IDLE
+        self.agent_hooks = agent_hooks
+        self.run_context = run_context
+
+        self.api_key = provider_config.get("coze_api_key", "")
+        if not self.api_key:
+            raise Exception("Coze API Key 不能为空。")
+        self.bot_id = provider_config.get("bot_id", "")
+        if not self.bot_id:
+            raise Exception("Coze Bot ID 不能为空。")
+        self.api_base: str = provider_config.get("coze_api_base", "https://api.coze.cn")
+
+        if not isinstance(self.api_base, str) or not self.api_base.startswith(
+            ("http://", "https://"),
+        ):
+            raise Exception(
+                "Coze API Base URL 格式不正确，必须以 http:// 或 https:// 开头。",
+            )
+
+        self.timeout = provider_config.get("timeout", 120)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+        self.auto_save_history = provider_config.get("auto_save_history", True)
+
+        # 创建 API 客户端
+        self.api_client = CozeAPIClient(api_key=self.api_key, api_base=self.api_base)
+
+        # 会话相关缓存
+        self.file_id_cache: dict[str, dict[str, str]] = {}
+
+    @override
+    async def step(self):
+        """
+        执行 Coze Agent 的一个步骤
+        """
+        if not self.req:
+            raise ValueError("Request is not set. Please call reset() first.")
+
+        if self._state == AgentState.IDLE:
+            try:
+                await self.agent_hooks.on_agent_begin(self.run_context)
+            except Exception as e:
+                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
+
+        # 开始处理，转换到运行状态
+        self._transition_state(AgentState.RUNNING)
+
+        try:
+            # 执行 Coze 请求并处理结果
+            async for response in self._execute_coze_request():
+                yield response
+        except Exception as e:
+            logger.error(f"Coze 请求失败：{str(e)}")
+            self._transition_state(AgentState.ERROR)
+            self.final_llm_resp = LLMResponse(
+                role="err", completion_text=f"Coze 请求失败：{str(e)}"
+            )
+            yield AgentResponse(
+                type="err",
+                data=AgentResponseData(
+                    chain=MessageChain().message(f"Coze 请求失败：{str(e)}")
+                ),
+            )
+        finally:
+            await self.api_client.close()
+
+    @override
+    async def step_until_done(
+        self, max_step: int = 30
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        while not self.done():
+            async for resp in self.step():
+                yield resp
+
+    async def _execute_coze_request(self):
+        """执行 Coze 请求的核心逻辑"""
+        prompt = self.req.prompt or ""
+        session_id = self.req.session_id or "unknown"
+        image_urls = self.req.image_urls or []
+        contexts = self.req.contexts or []
+        system_prompt = self.req.system_prompt
+
+        # 用户ID参数
+        user_id = session_id
+
+        # 获取或创建会话ID
+        conversation_id = await sp.get_async(
+            scope="umo",
+            scope_id=user_id,
+            key="coze_conversation_id",
+            default="",
+        )
+
+        # 构建消息
+        additional_messages = []
+
+        if system_prompt:
+            if not self.auto_save_history or not conversation_id:
+                additional_messages.append(
+                    {
+                        "role": "system",
+                        "content": system_prompt,
+                        "content_type": "text",
+                    },
+                )
+
+        # 处理历史上下文
+        if not self.auto_save_history and contexts:
+            for ctx in contexts:
+                if isinstance(ctx, dict) and "role" in ctx and "content" in ctx:
+                    # 处理上下文中的图片
+                    content = ctx["content"]
+                    if isinstance(content, list):
+                        # 多模态内容，需要处理图片
+                        processed_content = []
+                        for item in content:
+                            if isinstance(item, dict):
+                                if item.get("type") == "text":
+                                    processed_content.append(item)
+                                elif item.get("type") == "image_url":
+                                    # 处理图片上传
+                                    try:
+                                        image_data = item.get("image_url", {})
+                                        url = image_data.get("url", "")
+                                        if url:
+                                            file_id = (
+                                                await self._download_and_upload_image(
+                                                    url, session_id
+                                                )
+                                            )
+                                            processed_content.append(
+                                                {
+                                                    "type": "file",
+                                                    "file_id": file_id,
+                                                    "file_url": url,
+                                                }
+                                            )
+                                    except Exception as e:
+                                        logger.warning(f"处理上下文图片失败: {e}")
+                                        continue
+
+                        if processed_content:
+                            additional_messages.append(
+                                {
+                                    "role": ctx["role"],
+                                    "content": processed_content,
+                                    "content_type": "object_string",
+                                }
+                            )
+                    else:
+                        # 纯文本内容
+                        additional_messages.append(
+                            {
+                                "role": ctx["role"],
+                                "content": content,
+                                "content_type": "text",
+                            }
+                        )
+
+        # 构建当前消息
+        if prompt or image_urls:
+            if image_urls:
+                # 多模态
+                object_string_content = []
+                if prompt:
+                    object_string_content.append({"type": "text", "text": prompt})
+
+                for url in image_urls:
+                    # the url is a base64 string
+                    try:
+                        image_data = base64.b64decode(url)
+                        file_id = await self.api_client.upload_file(image_data)
+                        object_string_content.append(
+                            {
+                                "type": "image",
+                                "file_id": file_id,
+                            }
+                        )
+                    except Exception as e:
+                        logger.warning(f"处理图片失败 {url}: {e}")
+                        continue
+
+                if object_string_content:
+                    content = json.dumps(object_string_content, ensure_ascii=False)
+                    additional_messages.append(
+                        {
+                            "role": "user",
+                            "content": content,
+                            "content_type": "object_string",
+                        }
+                    )
+            elif prompt:
+                # 纯文本
+                additional_messages.append(
+                    {
+                        "role": "user",
+                        "content": prompt,
+                        "content_type": "text",
+                    },
+                )
+
+        # 执行 Coze API 请求
+        accumulated_content = ""
+        message_started = False
+
+        async for chunk in self.api_client.chat_messages(
+            bot_id=self.bot_id,
+            user_id=user_id,
+            additional_messages=additional_messages,
+            conversation_id=conversation_id,
+            auto_save_history=self.auto_save_history,
+            stream=True,
+            timeout=self.timeout,
+        ):
+            event_type = chunk.get("event")
+            data = chunk.get("data", {})
+
+            if event_type == "conversation.chat.created":
+                if isinstance(data, dict) and "conversation_id" in data:
+                    await sp.put_async(
+                        scope="umo",
+                        scope_id=user_id,
+                        key="coze_conversation_id",
+                        value=data["conversation_id"],
+                    )
+
+            if event_type == "conversation.message.delta":
+                # 增量消息
+                content = data.get("content", "")
+                if not content and "delta" in data:
+                    content = data["delta"].get("content", "")
+                if not content and "text" in data:
+                    content = data.get("text", "")
+
+                if content:
+                    accumulated_content += content
+                    message_started = True
+
+                    # 如果是流式响应，发送增量数据
+                    if self.streaming:
+                        yield AgentResponse(
+                            type="streaming_delta",
+                            data=AgentResponseData(
+                                chain=MessageChain().message(content)
+                            ),
+                        )
+
+            elif event_type == "conversation.message.completed":
+                # 消息完成
+                logger.debug("Coze message completed")
+                message_started = True
+
+            elif event_type == "conversation.chat.completed":
+                # 对话完成
+                logger.debug("Coze chat completed")
+                break
+
+            elif event_type == "error":
+                # 错误处理
+                error_msg = data.get("msg", "未知错误")
+                error_code = data.get("code", "UNKNOWN")
+                logger.error(f"Coze 出现错误: {error_code} - {error_msg}")
+                raise Exception(f"Coze 出现错误: {error_code} - {error_msg}")
+
+        if not message_started and not accumulated_content:
+            logger.warning("Coze 未返回任何内容")
+            accumulated_content = ""
+
+        # 创建最终响应
+        chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
+        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
+        self._transition_state(AgentState.DONE)
+
+        try:
+            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
+        except Exception as e:
+            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
+
+        # 返回最终结果
+        yield AgentResponse(
+            type="llm_result",
+            data=AgentResponseData(chain=chain),
+        )
+
+    async def _download_and_upload_image(
+        self,
+        image_url: str,
+        session_id: str | None = None,
+    ) -> str:
+        """下载图片并上传到 Coze，返回 file_id"""
+        import hashlib
+
+        # 计算哈希实现缓存
+        cache_key = hashlib.md5(image_url.encode("utf-8")).hexdigest()
+
+        if session_id:
+            if session_id not in self.file_id_cache:
+                self.file_id_cache[session_id] = {}
+
+            if cache_key in self.file_id_cache[session_id]:
+                file_id = self.file_id_cache[session_id][cache_key]
+                logger.debug(f"[Coze] 使用缓存的 file_id: {file_id}")
+                return file_id
+
+        try:
+            image_data = await self.api_client.download_image(image_url)
+            file_id = await self.api_client.upload_file(image_data)
+
+            if session_id:
+                self.file_id_cache[session_id][cache_key] = file_id
+                logger.debug(f"[Coze] 图片上传成功并缓存，file_id: {file_id}")
+
+            return file_id
+
+        except Exception as e:
+            logger.error(f"处理图片失败 {image_url}: {e!s}")
+            raise Exception(f"处理图片失败: {e!s}")
+
+    @override
+    def done(self) -> bool:
+        """检查 Agent 是否已完成工作"""
+        return self._state in (AgentState.DONE, AgentState.ERROR)
+
+    @override
+    def get_final_llm_resp(self) -> LLMResponse | None:
+        return self.final_llm_resp
@@ -0,0 +1,403 @@
+import asyncio
+import functools
+import queue
+import re
+import sys
+import threading
+import typing as T
+
+from dashscope import Application
+from dashscope.app.application_response import ApplicationResponse
+
+import astrbot.core.message.components as Comp
+from astrbot.core import logger, sp
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import (
+    LLMResponse,
+    ProviderRequest,
+)
+
+from ...hooks import BaseAgentRunHooks
+from ...response import AgentResponseData
+from ...run_context import ContextWrapper, TContext
+from ..base import AgentResponse, AgentState, BaseAgentRunner
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
+
+class DashscopeAgentRunner(BaseAgentRunner[TContext]):
+    """Dashscope Agent Runner"""
+
+    @override
+    async def reset(
+        self,
+        request: ProviderRequest,
+        run_context: ContextWrapper[TContext],
+        agent_hooks: BaseAgentRunHooks[TContext],
+        provider_config: dict,
+        **kwargs: T.Any,
+    ) -> None:
+        self.req = request
+        self.streaming = kwargs.get("streaming", False)
+        self.final_llm_resp = None
+        self._state = AgentState.IDLE
+        self.agent_hooks = agent_hooks
+        self.run_context = run_context
+
+        self.api_key = provider_config.get("dashscope_api_key", "")
+        if not self.api_key:
+            raise Exception("阿里云百炼 API Key 不能为空。")
+        self.app_id = provider_config.get("dashscope_app_id", "")
+        if not self.app_id:
+            raise Exception("阿里云百炼 APP ID 不能为空。")
+        self.dashscope_app_type = provider_config.get("dashscope_app_type", "")
+        if not self.dashscope_app_type:
+            raise Exception("阿里云百炼 APP 类型不能为空。")
+
+        self.variables: dict = provider_config.get("variables", {}) or {}
+        self.rag_options: dict = provider_config.get("rag_options", {})
+        self.output_reference = self.rag_options.get("output_reference", False)
+        self.rag_options = self.rag_options.copy()
+        self.rag_options.pop("output_reference", None)
+
+        self.timeout = provider_config.get("timeout", 120)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+
+    def has_rag_options(self):
+        """判断是否有 RAG 选项
+
+        Returns:
+            bool: 是否有 RAG 选项
+
+        """
+        if self.rag_options and (
+            len(self.rag_options.get("pipeline_ids", [])) > 0
+            or len(self.rag_options.get("file_ids", [])) > 0
+        ):
+            return True
+        return False
+
+    @override
+    async def step(self):
+        """
+        执行 Dashscope Agent 的一个步骤
+        """
+        if not self.req:
+            raise ValueError("Request is not set. Please call reset() first.")
+
+        if self._state == AgentState.IDLE:
+            try:
+                await self.agent_hooks.on_agent_begin(self.run_context)
+            except Exception as e:
+                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
+
+        # 开始处理，转换到运行状态
+        self._transition_state(AgentState.RUNNING)
+
+        try:
+            # 执行 Dashscope 请求并处理结果
+            async for response in self._execute_dashscope_request():
+                yield response
+        except Exception as e:
+            logger.error(f"阿里云百炼请求失败：{str(e)}")
+            self._transition_state(AgentState.ERROR)
+            self.final_llm_resp = LLMResponse(
+                role="err", completion_text=f"阿里云百炼请求失败：{str(e)}"
+            )
+            yield AgentResponse(
+                type="err",
+                data=AgentResponseData(
+                    chain=MessageChain().message(f"阿里云百炼请求失败：{str(e)}")
+                ),
+            )
+
+    @override
+    async def step_until_done(
+        self, max_step: int = 30
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        while not self.done():
+            async for resp in self.step():
+                yield resp
+
+    def _consume_sync_generator(
+        self, response: T.Any, response_queue: queue.Queue
+    ) -> None:
+        """在线程中消费同步generator,将结果放入队列
+
+        Args:
+            response: 同步generator对象
+            response_queue: 用于传递数据的队列
+
+        """
+        try:
+            if self.streaming:
+                for chunk in response:
+                    response_queue.put(("data", chunk))
+            else:
+                response_queue.put(("data", response))
+        except Exception as e:
+            response_queue.put(("error", e))
+        finally:
+            response_queue.put(("done", None))
+
+    async def _process_stream_chunk(
+        self, chunk: ApplicationResponse, output_text: str
+    ) -> tuple[str, list | None, AgentResponse | None]:
+        """处理流式响应的单个chunk
+
+        Args:
+            chunk: Dashscope响应chunk
+            output_text: 当前累积的输出文本
+
+        Returns:
+            (更新后的output_text, doc_references, AgentResponse或None)
+
+        """
+        logger.debug(f"dashscope stream chunk: {chunk}")
+
+        if chunk.status_code != 200:
+            logger.error(
+                f"阿里云百炼请求失败: request_id={chunk.request_id}, code={chunk.status_code}, message={chunk.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code",
+            )
+            self._transition_state(AgentState.ERROR)
+            error_msg = (
+                f"阿里云百炼请求失败: message={chunk.message} code={chunk.status_code}"
+            )
+            self.final_llm_resp = LLMResponse(
+                role="err",
+                result_chain=MessageChain().message(error_msg),
+            )
+            return (
+                output_text,
+                None,
+                AgentResponse(
+                    type="err",
+                    data=AgentResponseData(chain=MessageChain().message(error_msg)),
+                ),
+            )
+
+        chunk_text = chunk.output.get("text", "") or ""
+        # RAG 引用脚标格式化
+        chunk_text = re.sub(r"<ref>\[(\d+)\]</ref>", r"[\1]", chunk_text)
+
+        response = None
+        if chunk_text:
+            output_text += chunk_text
+            response = AgentResponse(
+                type="streaming_delta",
+                data=AgentResponseData(chain=MessageChain().message(chunk_text)),
+            )
+
+        # 获取文档引用
+        doc_references = chunk.output.get("doc_references", None)
+
+        return output_text, doc_references, response
+
+    def _format_doc_references(self, doc_references: list) -> str:
+        """格式化文档引用为文本
+
+        Args:
+            doc_references: 文档引用列表
+
+        Returns:
+            格式化后的引用文本
+
+        """
+        ref_parts = []
+        for ref in doc_references:
+            ref_title = (
+                ref.get("title", "") if ref.get("title") else ref.get("doc_name", "")
+            )
+            ref_parts.append(f"{ref['index_id']}. {ref_title}\n")
+        ref_str = "".join(ref_parts)
+        return f"\n\n回答来源:\n{ref_str}"
+
+    async def _build_request_payload(
+        self, prompt: str, session_id: str, contexts: list, system_prompt: str
+    ) -> dict:
+        """构建请求payload
+
+        Args:
+            prompt: 用户输入
+            session_id: 会话ID
+            contexts: 上下文列表
+            system_prompt: 系统提示词
+
+        Returns:
+            请求payload字典
+
+        """
+        conversation_id = await sp.get_async(
+            scope="umo",
+            scope_id=session_id,
+            key="dashscope_conversation_id",
+            default="",
+        )
+        # 获得会话变量
+        payload_vars = self.variables.copy()
+        session_var = await sp.get_async(
+            scope="umo",
+            scope_id=session_id,
+            key="session_variables",
+            default={},
+        )
+        payload_vars.update(session_var)
+
+        if (
+            self.dashscope_app_type in ["agent", "dialog-workflow"]
+            and not self.has_rag_options()
+        ):
+            # 支持多轮对话的
+            p = {
+                "app_id": self.app_id,
+                "api_key": self.api_key,
+                "prompt": prompt,
+                "biz_params": payload_vars or None,
+                "stream": self.streaming,
+                "incremental_output": True,
+            }
+            if conversation_id:
+                p["session_id"] = conversation_id
+            return p
+        else:
+            # 不支持多轮对话的
+            payload = {
+                "app_id": self.app_id,
+                "prompt": prompt,
+                "api_key": self.api_key,
+                "biz_params": payload_vars or None,
+                "stream": self.streaming,
+                "incremental_output": True,
+            }
+            if self.rag_options:
+                payload["rag_options"] = self.rag_options
+            return payload
+
+    async def _handle_streaming_response(
+        self, response: T.Any, session_id: str
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        """处理流式响应
+
+        Args:
+            response: Dashscope 流式响应 generator
+
+        Yields:
+            AgentResponse 对象
+
+        """
+        response_queue = queue.Queue()
+        consumer_thread = threading.Thread(
+            target=self._consume_sync_generator,
+            args=(response, response_queue),
+            daemon=True,
+        )
+        consumer_thread.start()
+
+        output_text = ""
+        doc_references = None
+
+        while True:
+            try:
+                item_type, item_data = await asyncio.get_event_loop().run_in_executor(
+                    None, response_queue.get, True, 1
+                )
+            except queue.Empty:
+                continue
+
+            if item_type == "done":
+                break
+            elif item_type == "error":
+                raise item_data
+            elif item_type == "data":
+                chunk = item_data
+                assert isinstance(chunk, ApplicationResponse)
+
+                (
+                    output_text,
+                    chunk_doc_refs,
+                    response,
+                ) = await self._process_stream_chunk(chunk, output_text)
+
+                if response:
+                    if response.type == "err":
+                        yield response
+                        return
+                    yield response
+
+                if chunk_doc_refs:
+                    doc_references = chunk_doc_refs
+
+                if chunk.output.session_id:
+                    await sp.put_async(
+                        scope="umo",
+                        scope_id=session_id,
+                        key="dashscope_conversation_id",
+                        value=chunk.output.session_id,
+                    )
+
+        # 添加 RAG 引用
+        if self.output_reference and doc_references:
+            ref_text = self._format_doc_references(doc_references)
+            output_text += ref_text
+
+            if self.streaming:
+                yield AgentResponse(
+                    type="streaming_delta",
+                    data=AgentResponseData(chain=MessageChain().message(ref_text)),
+                )
+
+        # 创建最终响应
+        chain = MessageChain(chain=[Comp.Plain(output_text)])
+        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
+        self._transition_state(AgentState.DONE)
+
+        try:
+            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
+        except Exception as e:
+            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
+
+        # 返回最终结果
+        yield AgentResponse(
+            type="llm_result",
+            data=AgentResponseData(chain=chain),
+        )
+
+    async def _execute_dashscope_request(self):
+        """执行 Dashscope 请求的核心逻辑"""
+        prompt = self.req.prompt or ""
+        session_id = self.req.session_id or "unknown"
+        image_urls = self.req.image_urls or []
+        contexts = self.req.contexts or []
+        system_prompt = self.req.system_prompt
+
+        # 检查图片输入
+        if image_urls:
+            logger.warning("阿里云百炼暂不支持图片输入，将自动忽略图片内容。")
+
+        # 构建请求payload
+        payload = await self._build_request_payload(
+            prompt, session_id, contexts, system_prompt
+        )
+
+        if not self.streaming:
+            payload["incremental_output"] = False
+
+        # 发起请求
+        partial = functools.partial(Application.call, **payload)
+        response = await asyncio.get_event_loop().run_in_executor(None, partial)
+
+        async for resp in self._handle_streaming_response(response, session_id):
+            yield resp
+
+    @override
+    def done(self) -> bool:
+        """检查 Agent 是否已完成工作"""
+        return self._state in (AgentState.DONE, AgentState.ERROR)
+
+    @override
+    def get_final_llm_resp(self) -> LLMResponse | None:
+        return self.final_llm_resp
@@ -0,0 +1,336 @@
+import base64
+import os
+import sys
+import typing as T
+
+import astrbot.core.message.components as Comp
+from astrbot.core import logger, sp
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import (
+    LLMResponse,
+    ProviderRequest,
+)
+from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot.core.utils.io import download_file
+
+from ...hooks import BaseAgentRunHooks
+from ...response import AgentResponseData
+from ...run_context import ContextWrapper, TContext
+from ..base import AgentResponse, AgentState, BaseAgentRunner
+from .dify_api_client import DifyAPIClient
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
+
+class DifyAgentRunner(BaseAgentRunner[TContext]):
+    """Dify Agent Runner"""
+
+    @override
+    async def reset(
+        self,
+        request: ProviderRequest,
+        run_context: ContextWrapper[TContext],
+        agent_hooks: BaseAgentRunHooks[TContext],
+        provider_config: dict,
+        **kwargs: T.Any,
+    ) -> None:
+        self.req = request
+        self.streaming = kwargs.get("streaming", False)
+        self.final_llm_resp = None
+        self._state = AgentState.IDLE
+        self.agent_hooks = agent_hooks
+        self.run_context = run_context
+
+        self.api_key = provider_config.get("dify_api_key", "")
+        self.api_base = provider_config.get("dify_api_base", "https://api.dify.ai/v1")
+        self.api_type = provider_config.get("dify_api_type", "chat")
+        self.workflow_output_key = provider_config.get(
+            "dify_workflow_output_key",
+            "astrbot_wf_output",
+        )
+        self.dify_query_input_key = provider_config.get(
+            "dify_query_input_key",
+            "astrbot_text_query",
+        )
+        self.variables: dict = provider_config.get("variables", {}) or {}
+        self.timeout = provider_config.get("timeout", 60)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+
+        self.api_client = DifyAPIClient(self.api_key, self.api_base)
+
+    @override
+    async def step(self):
+        """
+        执行 Dify Agent 的一个步骤
+        """
+        if not self.req:
+            raise ValueError("Request is not set. Please call reset() first.")
+
+        if self._state == AgentState.IDLE:
+            try:
+                await self.agent_hooks.on_agent_begin(self.run_context)
+            except Exception as e:
+                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
+
+        # 开始处理，转换到运行状态
+        self._transition_state(AgentState.RUNNING)
+
+        try:
+            # 执行 Dify 请求并处理结果
+            async for response in self._execute_dify_request():
+                yield response
+        except Exception as e:
+            logger.error(f"Dify 请求失败：{str(e)}")
+            self._transition_state(AgentState.ERROR)
+            self.final_llm_resp = LLMResponse(
+                role="err", completion_text=f"Dify 请求失败：{str(e)}"
+            )
+            yield AgentResponse(
+                type="err",
+                data=AgentResponseData(
+                    chain=MessageChain().message(f"Dify 请求失败：{str(e)}")
+                ),
+            )
+        finally:
+            await self.api_client.close()
+
+    @override
+    async def step_until_done(
+        self, max_step: int = 30
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        while not self.done():
+            async for resp in self.step():
+                yield resp
+
+    async def _execute_dify_request(self):
+        """执行 Dify 请求的核心逻辑"""
+        prompt = self.req.prompt or ""
+        session_id = self.req.session_id or "unknown"
+        image_urls = self.req.image_urls or []
+        system_prompt = self.req.system_prompt
+
+        conversation_id = await sp.get_async(
+            scope="umo",
+            scope_id=session_id,
+            key="dify_conversation_id",
+            default="",
+        )
+        result = ""
+
+        # 处理图片上传
+        files_payload = []
+        for image_url in image_urls:
+            # image_url is a base64 string
+            try:
+                image_data = base64.b64decode(image_url)
+                file_response = await self.api_client.file_upload(
+                    file_data=image_data,
+                    user=session_id,
+                    mime_type="image/png",
+                    file_name="image.png",
+                )
+                logger.debug(f"Dify 上传图片响应：{file_response}")
+                if "id" not in file_response:
+                    logger.warning(
+                        f"上传图片后得到未知的 Dify 响应：{file_response}，图片将忽略。"
+                    )
+                    continue
+                files_payload.append(
+                    {
+                        "type": "image",
+                        "transfer_method": "local_file",
+                        "upload_file_id": file_response["id"],
+                    }
+                )
+            except Exception as e:
+                logger.warning(f"上传图片失败：{e}")
+                continue
+
+        # 获得会话变量
+        payload_vars = self.variables.copy()
+        # 动态变量
+        session_var = await sp.get_async(
+            scope="umo",
+            scope_id=session_id,
+            key="session_variables",
+            default={},
+        )
+        payload_vars.update(session_var)
+        payload_vars["system_prompt"] = system_prompt
+
+        # 处理不同的 API 类型
+        match self.api_type:
+            case "chat" | "agent" | "chatflow":
+                if not prompt:
+                    prompt = "请描述这张图片。"
+
+                async for chunk in self.api_client.chat_messages(
+                    inputs={
+                        **payload_vars,
+                    },
+                    query=prompt,
+                    user=session_id,
+                    conversation_id=conversation_id,
+                    files=files_payload,
+                    timeout=self.timeout,
+                ):
+                    logger.debug(f"dify resp chunk: {chunk}")
+                    if chunk["event"] == "message" or chunk["event"] == "agent_message":
+                        result += chunk["answer"]
+                        if not conversation_id:
+                            await sp.put_async(
+                                scope="umo",
+                                scope_id=session_id,
+                                key="dify_conversation_id",
+                                value=chunk["conversation_id"],
+                            )
+                            conversation_id = chunk["conversation_id"]
+
+                        # 如果是流式响应，发送增量数据
+                        if self.streaming and chunk["answer"]:
+                            yield AgentResponse(
+                                type="streaming_delta",
+                                data=AgentResponseData(
+                                    chain=MessageChain().message(chunk["answer"])
+                                ),
+                            )
+                    elif chunk["event"] == "message_end":
+                        logger.debug("Dify message end")
+                        break
+                    elif chunk["event"] == "error":
+                        logger.error(f"Dify 出现错误：{chunk}")
+                        raise Exception(
+                            f"Dify 出现错误 status: {chunk['status']} message: {chunk['message']}"
+                        )
+
+            case "workflow":
+                async for chunk in self.api_client.workflow_run(
+                    inputs={
+                        self.dify_query_input_key: prompt,
+                        "astrbot_session_id": session_id,
+                        **payload_vars,
+                    },
+                    user=session_id,
+                    files=files_payload,
+                    timeout=self.timeout,
+                ):
+                    logger.debug(f"dify workflow resp chunk: {chunk}")
+                    match chunk["event"]:
+                        case "workflow_started":
+                            logger.info(
+                                f"Dify 工作流(ID: {chunk['workflow_run_id']})开始运行。"
+                            )
+                        case "node_finished":
+                            logger.debug(
+                                f"Dify 工作流节点(ID: {chunk['data']['node_id']} Title: {chunk['data'].get('title', '')})运行结束。"
+                            )
+                        case "text_chunk":
+                            if self.streaming and chunk["data"]["text"]:
+                                yield AgentResponse(
+                                    type="streaming_delta",
+                                    data=AgentResponseData(
+                                        chain=MessageChain().message(
+                                            chunk["data"]["text"]
+                                        )
+                                    ),
+                                )
+                        case "workflow_finished":
+                            logger.info(
+                                f"Dify 工作流(ID: {chunk['workflow_run_id']})运行结束"
+                            )
+                            logger.debug(f"Dify 工作流结果：{chunk}")
+                            if chunk["data"]["error"]:
+                                logger.error(
+                                    f"Dify 工作流出现错误：{chunk['data']['error']}"
+                                )
+                                raise Exception(
+                                    f"Dify 工作流出现错误：{chunk['data']['error']}"
+                                )
+                            if self.workflow_output_key not in chunk["data"]["outputs"]:
+                                raise Exception(
+                                    f"Dify 工作流的输出不包含指定的键名：{self.workflow_output_key}"
+                                )
+                            result = chunk
+            case _:
+                raise Exception(f"未知的 Dify API 类型：{self.api_type}")
+
+        if not result:
+            logger.warning("Dify 请求结果为空，请查看 Debug 日志。")
+
+        # 解析结果
+        chain = await self.parse_dify_result(result)
+
+        # 创建最终响应
+        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
+        self._transition_state(AgentState.DONE)
+
+        try:
+            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
+        except Exception as e:
+            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
+
+        # 返回最终结果
+        yield AgentResponse(
+            type="llm_result",
+            data=AgentResponseData(chain=chain),
+        )
+
+    async def parse_dify_result(self, chunk: dict | str) -> MessageChain:
+        """解析 Dify 的响应结果"""
+        if isinstance(chunk, str):
+            # Chat
+            return MessageChain(chain=[Comp.Plain(chunk)])
+
+        async def parse_file(item: dict):
+            match item["type"]:
+                case "image":
+                    return Comp.Image(file=item["url"], url=item["url"])
+                case "audio":
+                    # 仅支持 wav
+                    temp_dir = os.path.join(get_astrbot_data_path(), "temp")
+                    path = os.path.join(temp_dir, f"{item['filename']}.wav")
+                    await download_file(item["url"], path)
+                    return Comp.Image(file=item["url"], url=item["url"])
+                case "video":
+                    return Comp.Video(file=item["url"])
+                case _:
+                    return Comp.File(name=item["filename"], file=item["url"])
+
+        output = chunk["data"]["outputs"][self.workflow_output_key]
+        chains = []
+        if isinstance(output, str):
+            # 纯文本输出
+            chains.append(Comp.Plain(output))
+        elif isinstance(output, list):
+            # 主要适配 Dify 的 HTTP 请求结点的多模态输出
+            for item in output:
+                # handle Array[File]
+                if (
+                    not isinstance(item, dict)
+                    or item.get("dify_model_identity", "") != "__dify__file__"
+                ):
+                    chains.append(Comp.Plain(str(output)))
+                    break
+        else:
+            chains.append(Comp.Plain(str(output)))
+
+        # scan file
+        files = chunk["data"].get("files", [])
+        for item in files:
+            comp = await parse_file(item)
+            chains.append(comp)
+
+        return MessageChain(chain=chains)
+
+    @override
+    def done(self) -> bool:
+        """检查 Agent 是否已完成工作"""
+        return self._state in (AgentState.DONE, AgentState.ERROR)
+
+    @override
+    def get_final_llm_resp(self) -> LLMResponse | None:
+        return self.final_llm_resp
@@ -3,7 +3,7 @@ import json
 from collections.abc import AsyncGenerator
 from typing import Any

-from aiohttp import ClientResponse, ClientSession
+from aiohttp import ClientResponse, ClientSession, FormData

 from astrbot.core import logger

@@ -101,21 +101,59 @@ class DifyAPIClient:

    async def file_upload(
        self,
-        file_path: str,
        user: str,
+        file_path: str | None = None,
+        file_data: bytes | None = None,
+        file_name: str | None = None,
+        mime_type: str | None = None,
    ) -> dict[str, Any]:
+        """Upload a file to Dify. Must provide either file_path or file_data.
+
+        Args:
+            user: The user ID.
+            file_path: The path to the file to upload.
+            file_data: The file data in bytes.
+            file_name: Optional file name when using file_data.
+        Returns:
+            A dictionary containing the uploaded file information.
+        """
        url = f"{self.api_base}/files/upload"
-        with open(file_path, "rb") as f:
-            payload = {
-                "user": user,
-                "file": f,
-            }
-            async with self.session.post(
-                url,
-                data=payload,
-                headers=self.headers,
-            ) as resp:
-                return await resp.json()  # {"id": "xxx", ...}
+
+        form = FormData()
+        form.add_field("user", user)
+
+        if file_data is not None:
+            # 使用 bytes 数据
+            form.add_field(
+                "file",
+                file_data,
+                filename=file_name or "uploaded_file",
+                content_type=mime_type or "application/octet-stream",
+            )
+        elif file_path is not None:
+            # 使用文件路径
+            import os
+
+            with open(file_path, "rb") as f:
+                file_content = f.read()
+                form.add_field(
+                    "file",
+                    file_content,
+                    filename=os.path.basename(file_path),
+                    content_type=mime_type or "application/octet-stream",
+                )
+        else:
+            raise ValueError("file_path 和 file_data 不能同时为 None")
+
+        async with self.session.post(
+            url,
+            data=form,
+            headers=self.headers,  # 不包含 Content-Type，让 aiohttp 自动设置
+        ) as resp:
+            if resp.status != 200 and resp.status != 201:
+                text = await resp.text()
+                raise Exception(f"Dify 文件上传失败：{resp.status}. {text}")
+            return await resp.json()  # {"id": "xxx", ...}

    async def close(self):
        await self.session.close()
@@ -23,7 +23,7 @@ from astrbot.core.provider.entities import (
 from astrbot.core.provider.provider import Provider

 from ..hooks import BaseAgentRunHooks
-from ..message import AssistantMessageSegment, ToolCallMessageSegment
+from ..message import AssistantMessageSegment, Message, ToolCallMessageSegment
 from ..response import AgentResponseData
 from ..run_context import ContextWrapper, TContext
 from ..tool_executor import BaseFunctionToolExecutor
@@ -55,11 +55,19 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
        self.agent_hooks = agent_hooks
        self.run_context = run_context

-    def _transition_state(self, new_state: AgentState) -> None:
-        """转换 Agent 状态"""
-        if self._state != new_state:
-            logger.debug(f"Agent state transition: {self._state} -> {new_state}")
-            self._state = new_state
+        messages = []
+        # append existing messages in the run context
+        for msg in request.contexts:
+            messages.append(Message.model_validate(msg))
+        if request.prompt is not None:
+            m = await request.assemble_context()
+            messages.append(Message.model_validate(m))
+        if request.system_prompt:
+            messages.insert(
+                0,
+                Message(role="system", content=request.system_prompt),
+            )
+        self.run_context.messages = messages

    async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
        """Yields chunks *and* a final LLMResponse."""
@@ -89,20 +97,28 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
        llm_resp_result = None

        async for llm_response in self._iter_llm_responses():
-            assert isinstance(llm_response, LLMResponse)
            if llm_response.is_chunk:
                if llm_response.result_chain:
                    yield AgentResponse(
                        type="streaming_delta",
                        data=AgentResponseData(chain=llm_response.result_chain),
                    )
-                else:
+                elif llm_response.completion_text:
                    yield AgentResponse(
                        type="streaming_delta",
                        data=AgentResponseData(
                            chain=MessageChain().message(llm_response.completion_text),
                        ),
                    )
+                elif llm_response.reasoning_content:
+                    yield AgentResponse(
+                        type="streaming_delta",
+                        data=AgentResponseData(
+                            chain=MessageChain(type="reasoning").message(
+                                llm_response.reasoning_content,
+                            ),
+                        ),
+                    )
                continue
            llm_resp_result = llm_response
            break  # got final response
@@ -130,6 +146,13 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
            # 如果没有工具调用，转换到完成状态
            self.final_llm_resp = llm_resp
            self._transition_state(AgentState.DONE)
+            # record the final assistant message
+            self.run_context.messages.append(
+                Message(
+                    role="assistant",
+                    content=llm_resp.completion_text or "",
+                ),
+            )
            try:
                await self.agent_hooks.on_agent_done(self.run_context, llm_resp)
            except Exception as e:
@@ -156,13 +179,16 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                yield AgentResponse(
                    type="tool_call",
                    data=AgentResponseData(
-                        chain=MessageChain().message(f"🔨 调用工具: {tool_call_name}"),
+                        chain=MessageChain(type="tool_call").message(
+                            f"🔨 调用工具: {tool_call_name}"
+                        ),
                    ),
                )
            async for result in self._handle_function_tools(self.req, llm_resp):
                if isinstance(result, list):
                    tool_call_result_blocks = result
                elif isinstance(result, MessageChain):
+                    result.type = "tool_call_result"
                    yield AgentResponse(
                        type="tool_call_result",
                        data=AgentResponseData(chain=result),
@@ -175,8 +201,23 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                ),
                tool_calls_result=tool_call_result_blocks,
            )
+            # record the assistant message with tool calls
+            self.run_context.messages.extend(
+                tool_calls_result.to_openai_messages_model()
+            )
+
            self.req.append_tool_calls_result(tool_calls_result)

+    async def step_until_done(
+        self, max_step: int
+    ) -> T.AsyncGenerator[AgentResponse, None]:
+        """Process steps until the agent is done."""
+        step_count = 0
+        while not self.done() and step_count < max_step:
+            step_count += 1
+            async for resp in self.step():
+                yield resp
+
    async def _handle_function_tools(
        self,
        req: ProviderRequest,
@@ -1,15 +1,18 @@
-from collections.abc import Awaitable, Callable
+from collections.abc import AsyncGenerator, Awaitable, Callable
 from typing import Any, Generic

 import jsonschema
 import mcp
 from deprecated import deprecated
-from pydantic import model_validator
+from pydantic import Field, model_validator
 from pydantic.dataclasses import dataclass

+from astrbot.core.message.message_event_result import MessageEventResult
+
 from .run_context import ContextWrapper, TContext

 ParametersType = dict[str, Any]
+ToolExecResult = str | mcp.types.CallToolResult


@dataclass
@@ -37,7 +40,10 @@ class ToolSchema:
 class FunctionTool(ToolSchema, Generic[TContext]):
    """A callable tool, for function calling."""

-    handler: Callable[..., Awaitable[Any]] | None = None
+    handler: (
+        Callable[..., Awaitable[str | None] | AsyncGenerator[MessageEventResult, None]]
+        | None
+    ) = None
    """a callable that implements the tool's functionality. It should be an async function."""

    handler_module_path: str | None = None
@@ -55,15 +61,14 @@ class FunctionTool(ToolSchema, Generic[TContext]):
    def __repr__(self):
        return f"FuncTool(name={self.name}, parameters={self.parameters}, description={self.description})"

-    async def call(
-        self, context: ContextWrapper[TContext], **kwargs
-    ) -> str | mcp.types.CallToolResult:
+    async def call(self, context: ContextWrapper[TContext], **kwargs) -> ToolExecResult:
        """Run the tool with the given arguments. The handler field has priority."""
        raise NotImplementedError(
            "FunctionTool.call() must be implemented by subclasses or set a handler."
        )


+@dataclass
 class ToolSet:
    """A set of function tools that can be used in function calling.

@@ -71,8 +76,7 @@ class ToolSet:
    convert the tools to different API formats (OpenAI, Anthropic, Google GenAI).
    """

-    def __init__(self, tools: list[FunctionTool] | None = None):
-        self.tools: list[FunctionTool] = tools or []
+    tools: list[FunctionTool] = Field(default_factory=list)

    def empty(self) -> bool:
        """Check if the tool set is empty."""
@@ -1,14 +1,19 @@
-from dataclasses import dataclass
+from pydantic import Field
+from pydantic.dataclasses import dataclass

+from astrbot.core.agent.run_context import ContextWrapper
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.provider import Provider
-from astrbot.core.provider.entities import ProviderRequest
+from astrbot.core.star.context import Context


-@dataclass
+@dataclass(config={"arbitrary_types_allowed": True})
 class AstrAgentContext:
-    provider: Provider
-    first_provider_request: ProviderRequest
-    curr_provider_request: ProviderRequest
-    streaming: bool
+    context: Context
+    """The star context instance"""
    event: AstrMessageEvent
+    """The message event associated with the agent context."""
+    extra: dict[str, str] = Field(default_factory=dict)
+    """Customized extra data."""
+
+
+AgentContextWrapper = ContextWrapper[AstrAgentContext]
@@ -0,0 +1,36 @@
+from typing import Any
+
+from mcp.types import CallToolResult
+
+from astrbot.core.agent.hooks import BaseAgentRunHooks
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.agent.tool import FunctionTool
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.pipeline.context_utils import call_event_hook
+from astrbot.core.star.star_handler import EventType
+
+
+class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
+    async def on_agent_done(self, run_context, llm_response):
+        # 执行事件钩子
+        await call_event_hook(
+            run_context.context.event,
+            EventType.OnLLMResponseEvent,
+            llm_response,
+        )
+
+    async def on_tool_end(
+        self,
+        run_context: ContextWrapper[AstrAgentContext],
+        tool: FunctionTool[Any],
+        tool_args: dict | None,
+        tool_result: CallToolResult | None,
+    ):
+        run_context.context.event.clear_result()
+
+
+class EmptyAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
+    pass
+
+
+MAIN_AGENT_HOOKS = MainAgentHooks()
@@ -0,0 +1,94 @@
+import traceback
+from collections.abc import AsyncGenerator
+
+from astrbot.core import logger
+from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.message.message_event_result import (
+    MessageChain,
+    MessageEventResult,
+    ResultContentType,
+)
+from astrbot.core.provider.entities import LLMResponse
+
+AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
+
+
+async def run_agent(
+    agent_runner: AgentRunner,
+    max_step: int = 30,
+    show_tool_use: bool = True,
+    stream_to_general: bool = False,
+    show_reasoning: bool = False,
+) -> AsyncGenerator[MessageChain | None, None]:
+    step_idx = 0
+    astr_event = agent_runner.run_context.context.event
+    while step_idx < max_step:
+        step_idx += 1
+        try:
+            async for resp in agent_runner.step():
+                if astr_event.is_stopped():
+                    return
+                if resp.type == "tool_call_result":
+                    msg_chain = resp.data["chain"]
+                    if msg_chain.type == "tool_direct_result":
+                        # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
+                        await astr_event.send(resp.data["chain"])
+                        continue
+                    # 对于其他情况，暂时先不处理
+                    continue
+                elif resp.type == "tool_call":
+                    if agent_runner.streaming:
+                        # 用来标记流式响应需要分节
+                        yield MessageChain(chain=[], type="break")
+                    if show_tool_use:
+                        await astr_event.send(resp.data["chain"])
+                    continue
+
+                if stream_to_general and resp.type == "streaming_delta":
+                    continue
+
+                if stream_to_general or not agent_runner.streaming:
+                    content_typ = (
+                        ResultContentType.LLM_RESULT
+                        if resp.type == "llm_result"
+                        else ResultContentType.GENERAL_RESULT
+                    )
+                    astr_event.set_result(
+                        MessageEventResult(
+                            chain=resp.data["chain"].chain,
+                            result_content_type=content_typ,
+                        ),
+                    )
+                    yield
+                    astr_event.clear_result()
+                elif resp.type == "streaming_delta":
+                    chain = resp.data["chain"]
+                    if chain.type == "reasoning" and not show_reasoning:
+                        # display the reasoning content only when configured
+                        continue
+                    yield resp.data["chain"]  # MessageChain
+            if agent_runner.done():
+                break
+
+        except Exception as e:
+            logger.error(traceback.format_exc())
+
+            err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在平台日志查看和分享错误详情。\n"
+
+            error_llm_response = LLMResponse(
+                role="err",
+                completion_text=err_msg,
+            )
+            try:
+                await agent_runner.agent_hooks.on_agent_done(
+                    agent_runner.run_context, error_llm_response
+                )
+            except Exception:
+                logger.exception("Error in on_agent_done hook")
+
+            if agent_runner.streaming:
+                yield MessageChain().message(err_msg)
+            else:
+                astr_event.set_result(MessageEventResult().message(err_msg))
+            return
@@ -0,0 +1,250 @@
+import asyncio
+import inspect
+import traceback
+import typing as T
+
+import mcp
+
+from astrbot import logger
+from astrbot.core.agent.handoff import HandoffTool
+from astrbot.core.agent.mcp_client import MCPTool
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.agent.tool import FunctionTool, ToolSet
+from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.message.message_event_result import (
+    CommandResult,
+    MessageChain,
+    MessageEventResult,
+)
+from astrbot.core.provider.register import llm_tools
+
+
+class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
+    @classmethod
+    async def execute(cls, tool, run_context, **tool_args):
+        """执行函数调用。
+
+        Args:
+            event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
+            **kwargs: 函数调用的参数。
+
+        Returns:
+            AsyncGenerator[None | mcp.types.CallToolResult, None]
+
+        """
+        if isinstance(tool, HandoffTool):
+            async for r in cls._execute_handoff(tool, run_context, **tool_args):
+                yield r
+            return
+
+        elif isinstance(tool, MCPTool):
+            async for r in cls._execute_mcp(tool, run_context, **tool_args):
+                yield r
+            return
+
+        else:
+            async for r in cls._execute_local(tool, run_context, **tool_args):
+                yield r
+            return
+
+    @classmethod
+    async def _execute_handoff(
+        cls,
+        tool: HandoffTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        input_ = tool_args.get("input")
+
+        # make toolset for the agent
+        tools = tool.agent.tools
+        if tools:
+            toolset = ToolSet()
+            for t in tools:
+                if isinstance(t, str):
+                    _t = llm_tools.get_func(t)
+                    if _t:
+                        toolset.add_tool(_t)
+                elif isinstance(t, FunctionTool):
+                    toolset.add_tool(t)
+        else:
+            toolset = None
+
+        ctx = run_context.context.context
+        event = run_context.context.event
+        umo = event.unified_msg_origin
+        prov_id = await ctx.get_current_chat_provider_id(umo)
+        llm_resp = await ctx.tool_loop_agent(
+            event=event,
+            chat_provider_id=prov_id,
+            prompt=input_,
+            system_prompt=tool.agent.instructions,
+            tools=toolset,
+            max_steps=30,
+            run_hooks=tool.agent.run_hooks,
+        )
+        yield mcp.types.CallToolResult(
+            content=[mcp.types.TextContent(type="text", text=llm_resp.completion_text)]
+        )
+
+    @classmethod
+    async def _execute_local(
+        cls,
+        tool: FunctionTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        event = run_context.context.event
+        if not event:
+            raise ValueError("Event must be provided for local function tools.")
+
+        is_override_call = False
+        for ty in type(tool).mro():
+            if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
+                is_override_call = True
+                break
+
+        # 检查 tool 下有没有 run 方法
+        if not tool.handler and not hasattr(tool, "run") and not is_override_call:
+            raise ValueError("Tool must have a valid handler or override 'run' method.")
+
+        awaitable = None
+        method_name = ""
+        if tool.handler:
+            awaitable = tool.handler
+            method_name = "decorator_handler"
+        elif is_override_call:
+            awaitable = tool.call
+            method_name = "call"
+        elif hasattr(tool, "run"):
+            awaitable = getattr(tool, "run")
+            method_name = "run"
+        if awaitable is None:
+            raise ValueError("Tool must have a valid handler or override 'run' method.")
+
+        wrapper = call_local_llm_tool(
+            context=run_context,
+            handler=awaitable,
+            method_name=method_name,
+            **tool_args,
+        )
+        while True:
+            try:
+                resp = await asyncio.wait_for(
+                    anext(wrapper),
+                    timeout=run_context.tool_call_timeout,
+                )
+                if resp is not None:
+                    if isinstance(resp, mcp.types.CallToolResult):
+                        yield resp
+                    else:
+                        text_content = mcp.types.TextContent(
+                            type="text",
+                            text=str(resp),
+                        )
+                        yield mcp.types.CallToolResult(content=[text_content])
+                else:
+                    # NOTE: Tool 在这里直接请求发送消息给用户
+                    # TODO: 是否需要判断 event.get_result() 是否为空?
+                    # 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
+                    if res := run_context.context.event.get_result():
+                        if res.chain:
+                            try:
+                                await event.send(
+                                    MessageChain(
+                                        chain=res.chain,
+                                        type="tool_direct_result",
+                                    )
+                                )
+                            except Exception as e:
+                                logger.error(
+                                    f"Tool 直接发送消息失败: {e}",
+                                    exc_info=True,
+                                )
+                    yield None
+            except asyncio.TimeoutError:
+                raise Exception(
+                    f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
+                )
+            except StopAsyncIteration:
+                break
+
+    @classmethod
+    async def _execute_mcp(
+        cls,
+        tool: FunctionTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        res = await tool.call(run_context, **tool_args)
+        if not res:
+            return
+        yield res
+
+
+async def call_local_llm_tool(
+    context: ContextWrapper[AstrAgentContext],
+    handler: T.Callable[
+        ...,
+        T.Awaitable[MessageEventResult | mcp.types.CallToolResult | str | None]
+        | T.AsyncGenerator[MessageEventResult | CommandResult | str | None, None],
+    ],
+    method_name: str,
+    *args,
+    **kwargs,
+) -> T.AsyncGenerator[T.Any, None]:
+    """执行本地 LLM 工具的处理函数并处理其返回结果"""
+    ready_to_call = None  # 一个协程或者异步生成器
+
+    trace_ = None
+
+    event = context.context.event
+
+    try:
+        if method_name == "run" or method_name == "decorator_handler":
+            ready_to_call = handler(event, *args, **kwargs)
+        elif method_name == "call":
+            ready_to_call = handler(context, *args, **kwargs)
+        else:
+            raise ValueError(f"未知的方法名: {method_name}")
+    except ValueError as e:
+        logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
+    except TypeError:
+        logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
+    except Exception as e:
+        trace_ = traceback.format_exc()
+        logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
+
+    if not ready_to_call:
+        return
+
+    if inspect.isasyncgen(ready_to_call):
+        _has_yielded = False
+        try:
+            async for ret in ready_to_call:
+                # 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
+                # 返回值只能是 MessageEventResult 或者 None（无返回值）
+                _has_yielded = True
+                if isinstance(ret, (MessageEventResult, CommandResult)):
+                    # 如果返回值是 MessageEventResult, 设置结果并继续
+                    event.set_result(ret)
+                    yield
+                else:
+                    # 如果返回值是 None, 则不设置结果并继续
+                    # 继续执行后续阶段
+                    yield ret
+            if not _has_yielded:
+                # 如果这个异步生成器没有执行到 yield 分支
+                yield
+        except Exception as e:
+            logger.error(f"Previous Error: {trace_}")
+            raise e
+    elif inspect.iscoroutine(ready_to_call):
+        # 如果只是一个协程, 直接执行
+        ret = await ready_to_call
+        if isinstance(ret, (MessageEventResult, CommandResult)):
+            event.set_result(ret)
+            yield
+        else:
+            yield ret
@@ -24,6 +24,10 @@ class AstrBotConfig(dict):
    - 如果传入了 schema，将会通过 schema 解析出 default_config，此时传入的 default_config 会被忽略。
    """

+    config_path: str
+    default_config: dict
+    schema: dict | None
+
    def __init__(
        self,
        config_path: str = ASTRBOT_CONFIG_PATH,
@@ -4,9 +4,18 @@ import os

 from astrbot.core.utils.astrbot_path import get_astrbot_data_path

-VERSION = "4.5.2"
+VERSION = "4.9.1"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")

+WEBHOOK_SUPPORTED_PLATFORMS = [
+    "qq_official_webhook",
+    "weixin_official_account",
+    "wecom",
+    "wecom_ai_bot",
+    "slack",
+    "lark",
+]
+
 # 默认配置
 DEFAULT_CONFIG = {
    "config_version": 2,
@@ -34,7 +43,15 @@ DEFAULT_CONFIG = {
            "interval": "1.5,3.5",
            "log_base": 2.6,
            "words_count_threshold": 150,
+            "split_mode": "regex",  # regex 或 words
            "regex": ".*?[。？！~…]+|.+$",
+            "split_words": [
+                "。",
+                "？",
+                "！",
+                "~",
+                "…",
+            ],  # 当 split_mode 为 words 时使用
            "content_cleanup_rule": "",
        },
        "no_permission_reply": True,
@@ -68,9 +85,19 @@ DEFAULT_CONFIG = {
        "dequeue_context_length": 1,
        "streaming_response": False,
        "show_tool_use_status": False,
-        "streaming_segmented": False,
+        "agent_runner_type": "local",
+        "dify_agent_runner_provider_id": "",
+        "coze_agent_runner_provider_id": "",
+        "dashscope_agent_runner_provider_id": "",
+        "unsupported_streaming_strategy": "realtime_segmenting",
+        "reachability_check": False,
        "max_agent_step": 30,
        "tool_call_timeout": 60,
+        "file_extract": {
+            "enable": False,
+            "provider": "moonshotai",
+            "moonshotai_api_key": "",
+        },
    },
    "provider_stt_settings": {
        "enable": False,
@@ -81,11 +108,13 @@ DEFAULT_CONFIG = {
        "provider_id": "",
        "dual_output": False,
        "use_file_service": False,
+        "trigger_probability": 1.0,
    },
    "provider_ltm_settings": {
        "group_icl_enable": False,
        "group_message_max_cnt": 300,
        "image_caption": False,
+        "image_caption_provider_id": "",
        "active_reply": {
            "enable": False,
            "method": "possibility_reply",
@@ -137,10 +166,21 @@ DEFAULT_CONFIG = {
    "kb_names": [],  # 默认知识库名称列表
    "kb_fusion_top_k": 20,  # 知识库检索融合阶段返回结果数量
    "kb_final_top_k": 5,  # 知识库检索最终返回结果数量
+    "kb_agentic_mode": False,
+    "disable_builtin_commands": False,
 }


-# 配置项的中文描述、值类型
+"""
+AstrBot v3 时代的配置元数据，目前仅承担以下功能：
+
+1. 保存配置时，配置项的类型验证
+2. WebUI 展示提供商和平台适配器模版
+
+WebUI 的配置文件在 `CONFIG_METADATA_3` 中。
+
+未来将会逐步淘汰此配置元数据。
+"""
 CONFIG_METADATA_2 = {
    "platform_group": {
        "metadata": {
@@ -164,6 +204,8 @@ CONFIG_METADATA_2 = {
                        "appid": "",
                        "secret": "",
                        "is_sandbox": False,
+                        "unified_webhook_mode": True,
+                        "webhook_uuid": "",
                        "callback_server_host": "0.0.0.0",
                        "port": 6196,
                    },
@@ -194,6 +236,8 @@ CONFIG_METADATA_2 = {
                        "token": "",
                        "encoding_aes_key": "",
                        "api_base_url": "https://api.weixin.qq.com/cgi-bin/",
+                        "unified_webhook_mode": True,
+                        "webhook_uuid": "",
                        "callback_server_host": "0.0.0.0",
                        "port": 6194,
                        "active_send_mode": False,
@@ -208,6 +252,8 @@ CONFIG_METADATA_2 = {
                        "encoding_aes_key": "",
                        "kf_name": "",
                        "api_base_url": "https://qyapi.weixin.qq.com/cgi-bin/",
+                        "unified_webhook_mode": True,
+                        "webhook_uuid": "",
                        "callback_server_host": "0.0.0.0",
                        "port": 6195,
                    },
@@ -220,6 +266,8 @@ CONFIG_METADATA_2 = {
                        "wecom_ai_bot_name": "",
                        "token": "",
                        "encoding_aes_key": "",
+                        "unified_webhook_mode": True,
+                        "webhook_uuid": "",
                        "callback_server_host": "0.0.0.0",
                        "port": 6198,
                    },
@@ -231,6 +279,10 @@ CONFIG_METADATA_2 = {
                        "app_id": "",
                        "app_secret": "",
                        "domain": "https://open.feishu.cn",
+                        "lark_connection_mode": "socket",  # webhook, socket
+                        "webhook_uuid": "",
+                        "lark_encrypt_key": "",
+                        "lark_verification_token": "",
                    },
                    "钉钉(DingTalk)": {
                        "id": "dingtalk",
@@ -287,6 +339,8 @@ CONFIG_METADATA_2 = {
                        "app_token": "",
                        "signing_secret": "",
                        "slack_connection_mode": "socket",  # webhook, socket
+                        "unified_webhook_mode": True,
+                        "webhook_uuid": "",
                        "slack_webhook_host": "0.0.0.0",
                        "slack_webhook_port": 6197,
                        "slack_webhook_path": "/astrbot-slack-webhook/callback",
@@ -322,6 +376,28 @@ CONFIG_METADATA_2 = {
                    #     "type": "string",
                    #     "options": ["fullscreen", "embedded"],
                    # },
+                    "lark_connection_mode": {
+                        "description": "订阅方式",
+                        "type": "string",
+                        "options": ["socket", "webhook"],
+                        "labels": ["长连接模式", "推送至服务器模式"],
+                    },
+                    "lark_encrypt_key": {
+                        "description": "Encrypt Key",
+                        "type": "string",
+                        "hint": "用于解密飞书回调数据的加密密钥",
+                        "condition": {
+                            "lark_connection_mode": "webhook",
+                        },
+                    },
+                    "lark_verification_token": {
+                        "description": "Verification Token",
+                        "type": "string",
+                        "hint": "用于验证飞书回调请求的令牌",
+                        "condition": {
+                            "lark_connection_mode": "webhook",
+                        },
+                    },
                    "is_sandbox": {
                        "description": "沙箱模式",
                        "type": "bool",
@@ -366,16 +442,28 @@ CONFIG_METADATA_2 = {
                        "description": "Slack Webhook Host",
                        "type": "string",
                        "hint": "Only valid when Slack connection mode is `webhook`.",
+                        "condition": {
+                            "slack_connection_mode": "webhook",
+                            "unified_webhook_mode": False,
+                        },
                    },
                    "slack_webhook_port": {
                        "description": "Slack Webhook Port",
                        "type": "int",
                        "hint": "Only valid when Slack connection mode is `webhook`.",
+                        "condition": {
+                            "slack_connection_mode": "webhook",
+                            "unified_webhook_mode": False,
+                        },
                    },
                    "slack_webhook_path": {
                        "description": "Slack Webhook Path",
                        "type": "string",
                        "hint": "Only valid when Slack connection mode is `webhook`.",
+                        "condition": {
+                            "slack_connection_mode": "webhook",
+                            "unified_webhook_mode": False,
+                        },
                    },
                    "active_send_mode": {
                        "description": "是否换用主动发送接口",
@@ -566,6 +654,33 @@ CONFIG_METADATA_2 = {
                        "type": "string",
                        "hint": "可选的 Discord 活动名称。留空则不设置活动。",
                    },
+                    "port": {
+                        "description": "回调服务器端口",
+                        "type": "int",
+                        "hint": "回调服务器端口。留空则不启用回调服务器。",
+                        "condition": {
+                            "unified_webhook_mode": False,
+                        },
+                    },
+                    "callback_server_host": {
+                        "description": "回调服务器主机",
+                        "type": "string",
+                        "hint": "回调服务器主机。留空则不启用回调服务器。",
+                        "condition": {
+                            "unified_webhook_mode": False,
+                        },
+                    },
+                    "unified_webhook_mode": {
+                        "description": "统一 Webhook 模式",
+                        "type": "bool",
+                        "hint": "启用后，将使用 AstrBot 统一 Webhook 入口，无需单独开启端口。回调地址为 /api/platform/webhook/{webhook_uuid}。",
+                    },
+                    "webhook_uuid": {
+                        "invisible": True,
+                        "description": "Webhook UUID",
+                        "type": "string",
+                        "hint": "统一 Webhook 模式下的唯一标识符，创建平台时自动生成。",
+                    },
                },
            },
            "platform_settings": {
@@ -633,7 +748,7 @@ CONFIG_METADATA_2 = {
                            },
                            "words_count_threshold": {
                                "type": "int",
-                                "hint": "超过这个字数的消息不会被分段回复。默认为 150",
+                                "hint": "分段回复的字数上限。只有字数小于此值的消息才会被分段，超过此值的长消息将直接发送（不分段）。默认为 150",
                            },
                            "regex": {
                                "type": "string",
@@ -740,6 +855,7 @@ CONFIG_METADATA_2 = {
                        "api_base": "https://api.openai.com/v1",
                        "timeout": 120,
                        "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                        "hint": "也兼容所有与 OpenAI API 兼容的服务。",
@@ -755,6 +871,7 @@ CONFIG_METADATA_2 = {
                        "api_base": "",
                        "timeout": 120,
                        "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -768,6 +885,7 @@ CONFIG_METADATA_2 = {
                        "api_base": "https://api.x.ai/v1",
                        "timeout": 120,
                        "model_config": {"model": "grok-2-latest", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "xai_native_search": False,
                        "modalities": ["text", "image", "tool_use"],
@@ -799,6 +917,7 @@ CONFIG_METADATA_2 = {
                        "key": ["ollama"],  # ollama 的 key 默认是 ollama
                        "api_base": "http://localhost:11434/v1",
                        "model_config": {"model": "llama3.1-8b", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -813,6 +932,7 @@ CONFIG_METADATA_2 = {
                        "model_config": {
                            "model": "llama-3.1-8b",
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -829,6 +949,7 @@ CONFIG_METADATA_2 = {
                            "model": "gemini-1.5-flash",
                            "temperature": 0.4,
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -870,6 +991,24 @@ CONFIG_METADATA_2 = {
                        "api_base": "https://api.deepseek.com/v1",
                        "timeout": 120,
                        "model_config": {"model": "deepseek-chat", "temperature": 0.4},
+                        "custom_headers": {},
+                        "custom_extra_body": {},
+                        "modalities": ["text", "tool_use"],
+                    },
+                    "Groq": {
+                        "id": "groq_default",
+                        "provider": "groq",
+                        "type": "groq_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "api_base": "https://api.groq.com/openai/v1",
+                        "timeout": 120,
+                        "model_config": {
+                            "model": "openai/gpt-oss-20b",
+                            "temperature": 0.4,
+                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "tool_use"],
                    },
@@ -883,6 +1022,7 @@ CONFIG_METADATA_2 = {
                        "api_base": "https://api.302.ai/v1",
                        "timeout": 120,
                        "model_config": {"model": "gpt-4.1-mini", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -899,6 +1039,7 @@ CONFIG_METADATA_2 = {
                            "model": "deepseek-ai/DeepSeek-V3",
                            "temperature": 0.4,
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -915,6 +1056,7 @@ CONFIG_METADATA_2 = {
                            "model": "deepseek/deepseek-r1",
                            "temperature": 0.4,
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                    },
                    "小马算力": {
@@ -930,6 +1072,7 @@ CONFIG_METADATA_2 = {
                            "model": "kimi-k2-instruct-0905",
                            "temperature": 0.7,
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                    },
                    "优云智算": {
@@ -944,6 +1087,7 @@ CONFIG_METADATA_2 = {
                        "model_config": {
                            "model": "moonshotai/Kimi-K2-Instruct",
                        },
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -957,6 +1101,7 @@ CONFIG_METADATA_2 = {
                        "timeout": 120,
                        "api_base": "https://api.moonshot.cn/v1",
                        "model_config": {"model": "moonshot-v1-8k", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -972,13 +1117,15 @@ CONFIG_METADATA_2 = {
                        "model_config": {
                            "model": "glm-4-flash",
                        },
+                        "custom_headers": {},
+                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
                    "Dify": {
                        "id": "dify_app_default",
                        "provider": "dify",
                        "type": "dify",
-                        "provider_type": "chat_completion",
+                        "provider_type": "agent_runner",
                        "enable": True,
                        "dify_api_type": "chat",
                        "dify_api_key": "",
@@ -992,20 +1139,20 @@ CONFIG_METADATA_2 = {
                    "Coze": {
                        "id": "coze",
                        "provider": "coze",
-                        "provider_type": "chat_completion",
+                        "provider_type": "agent_runner",
                        "type": "coze",
                        "enable": True,
                        "coze_api_key": "",
                        "bot_id": "",
                        "coze_api_base": "https://api.coze.cn",
                        "timeout": 60,
-                        "auto_save_history": True,
+                        # "auto_save_history": True,
                    },
                    "阿里云百炼应用": {
                        "id": "dashscope",
                        "provider": "dashscope",
                        "type": "dashscope",
-                        "provider_type": "chat_completion",
+                        "provider_type": "agent_runner",
                        "enable": True,
                        "dashscope_app_type": "agent",
                        "dashscope_api_key": "",
@@ -1028,6 +1175,7 @@ CONFIG_METADATA_2 = {
                        "timeout": 120,
                        "api_base": "https://api-inference.modelscope.cn/v1",
                        "model_config": {"model": "Qwen/Qwen3-32B", "temperature": 0.4},
+                        "custom_headers": {},
                        "custom_extra_body": {},
                        "modalities": ["text", "image", "tool_use"],
                    },
@@ -1040,6 +1188,7 @@ CONFIG_METADATA_2 = {
                        "key": [],
                        "api_base": "https://api.fastgpt.in/api/v1",
                        "timeout": 60,
+                        "custom_headers": {},
                        "custom_extra_body": {},
                    },
                    "Whisper(API)": {
@@ -1052,7 +1201,7 @@ CONFIG_METADATA_2 = {
                        "api_base": "",
                        "model": "whisper-1",
                    },
-                    "Whisper(本地加载)": {
+                    "Whisper(Local)": {
                        "hint": "启用前请 pip 安装 openai-whisper 库（N卡用户大约下载 2GB，主要是 torch 和 cuda，CPU 用户大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                        "provider": "openai",
                        "type": "openai_whisper_selfhost",
@@ -1061,7 +1210,7 @@ CONFIG_METADATA_2 = {
                        "id": "whisper_selfhost",
                        "model": "tiny",
                    },
-                    "SenseVoice(本地加载)": {
+                    "SenseVoice(Local)": {
                        "hint": "启用前请 pip 安装 funasr、funasr_onnx、torchaudio、torch、modelscope、jieba 库（默认使用CPU，大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                        "type": "sensevoice_stt_selfhost",
                        "provider": "sensevoice",
@@ -1096,7 +1245,7 @@ CONFIG_METADATA_2 = {
                        "pitch": "+0Hz",
                        "timeout": 20,
                    },
-                    "GSV TTS(本地加载)": {
+                    "GSV TTS(Local)": {
                        "id": "gsv_tts",
                        "enable": False,
                        "provider": "gpt_sovits",
@@ -1273,6 +1422,19 @@ CONFIG_METADATA_2 = {
                        "timeout": 20,
                        "launch_model_if_not_running": False,
                    },
+                    "阿里云百炼重排序": {
+                        "id": "bailian_rerank",
+                        "type": "bailian_rerank",
+                        "provider": "bailian",
+                        "provider_type": "rerank",
+                        "enable": True,
+                        "rerank_api_key": "",
+                        "rerank_api_base": "https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
+                        "rerank_model": "qwen3-rerank",
+                        "timeout": 30,
+                        "return_documents": False,
+                        "instruct": "",
+                    },
                    "Xinference STT": {
                        "id": "xinference_stt",
                        "type": "xinference_stt",
@@ -1307,6 +1469,16 @@ CONFIG_METADATA_2 = {
                        "description": "重排序模型名称",
                        "type": "string",
                    },
+                    "return_documents": {
+                        "description": "是否在排序结果中返回文档原文",
+                        "type": "bool",
+                        "hint": "默认值false，以减少网络传输开销。",
+                    },
+                    "instruct": {
+                        "description": "自定义排序任务类型说明",
+                        "type": "string",
+                        "hint": "仅在使用 qwen3-rerank 模型时生效。建议使用英文撰写。",
+                    },
                    "launch_model_if_not_running": {
                        "description": "模型未运行时自动启动",
                        "type": "bool",
@@ -1321,6 +1493,12 @@ CONFIG_METADATA_2 = {
                        "render_type": "checkbox",
                        "hint": "模型支持的模态。如所填写的模型不支持图像，请取消勾选图像。",
                    },
+                    "custom_headers": {
+                        "description": "自定义添加请求头",
+                        "type": "dict",
+                        "items": {},
+                        "hint": "此处添加的键值对将被合并到 OpenAI SDK 的 default_headers 中，用于自定义 HTTP 请求头。值必须为字符串。",
+                    },
                    "custom_extra_body": {
                        "description": "自定义请求体参数",
                        "type": "dict",
@@ -1843,7 +2021,6 @@ CONFIG_METADATA_2 = {
                    "enable": {
                        "description": "启用",
                        "type": "bool",
-                        "hint": "是否启用。",
                    },
                    "key": {
                        "description": "API Key",
@@ -1970,17 +2147,41 @@ CONFIG_METADATA_2 = {
                    "show_tool_use_status": {
                        "type": "bool",
                    },
-                    "streaming_segmented": {
-                        "type": "bool",
+                    "unsupported_streaming_strategy": {
+                        "type": "string",
+                    },
+                    "agent_runner_type": {
+                        "type": "string",
+                    },
+                    "dify_agent_runner_provider_id": {
+                        "type": "string",
+                    },
+                    "coze_agent_runner_provider_id": {
+                        "type": "string",
+                    },
+                    "dashscope_agent_runner_provider_id": {
+                        "type": "string",
                    },
                    "max_agent_step": {
-                        "description": "工具调用轮数上限",
                        "type": "int",
                    },
                    "tool_call_timeout": {
-                        "description": "工具调用超时时间（秒）",
                        "type": "int",
                    },
+                    "file_extract": {
+                        "type": "object",
+                        "items": {
+                            "enable": {
+                                "type": "bool",
+                            },
+                            "provider": {
+                                "type": "string",
+                            },
+                            "moonshotai_api_key": {
+                                "type": "string",
+                            },
+                        },
+                    },
                },
            },
            "provider_stt_settings": {
@@ -2009,6 +2210,9 @@ CONFIG_METADATA_2 = {
                    "use_file_service": {
                        "type": "bool",
                    },
+                    "trigger_probability": {
+                        "type": "float",
+                    },
                },
            },
            "provider_ltm_settings": {
@@ -2023,6 +2227,9 @@ CONFIG_METADATA_2 = {
                    "image_caption": {
                        "type": "bool",
                    },
+                    "image_caption_provider_id": {
+                        "type": "string",
+                    },
                    "image_caption_prompt": {
                        "type": "string",
                    },
@@ -2106,39 +2313,93 @@ CONFIG_METADATA_2 = {
            "kb_names": {"type": "list", "items": {"type": "string"}},
            "kb_fusion_top_k": {"type": "int", "default": 20},
            "kb_final_top_k": {"type": "int", "default": 5},
+            "kb_agentic_mode": {"type": "bool"},
        },
    },
 }


+"""
+v4.7.0 之后，name, description, hint 等字段已经实现 i18n 国际化。国际化资源文件位于：
+
+- dashboard/src/i18n/locales/en-US/features/config-metadata.json
+- dashboard/src/i18n/locales/zh-CN/features/config-metadata.json
+
+如果在此文件中添加了新的配置字段，请务必同步更新上述两个国际化资源文件。
+"""
 CONFIG_METADATA_3 = {
    "ai_group": {
        "name": "AI 配置",
        "metadata": {
-            "ai": {
-                "description": "模型",
+            "agent_runner": {
+                "description": "Agent 执行方式",
+                "hint": "选择 AI 对话的执行器，默认为 AstrBot 内置 Agent 执行器，可使用 AstrBot 内的知识库、人格、工具调用功能。如果不打算接入 Dify 或 Coze 等第三方 Agent 执行器，不需要修改此节。",
                "type": "object",
                "items": {
                    "provider_settings.enable": {
-                        "description": "启用大语言模型聊天",
+                        "description": "启用",
                        "type": "bool",
+                        "hint": "AI 对话总开关",
                    },
+                    "provider_settings.agent_runner_type": {
+                        "description": "执行器",
+                        "type": "string",
+                        "options": ["local", "dify", "coze", "dashscope"],
+                        "labels": ["内置 Agent", "Dify", "Coze", "阿里云百炼应用"],
+                        "condition": {
+                            "provider_settings.enable": True,
+                        },
+                    },
+                    "provider_settings.coze_agent_runner_provider_id": {
+                        "description": "Coze Agent 执行器提供商 ID",
+                        "type": "string",
+                        "_special": "select_agent_runner_provider:coze",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "coze",
+                            "provider_settings.enable": True,
+                        },
+                    },
+                    "provider_settings.dify_agent_runner_provider_id": {
+                        "description": "Dify Agent 执行器提供商 ID",
+                        "type": "string",
+                        "_special": "select_agent_runner_provider:dify",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "dify",
+                            "provider_settings.enable": True,
+                        },
+                    },
+                    "provider_settings.dashscope_agent_runner_provider_id": {
+                        "description": "阿里云百炼应用 Agent 执行器提供商 ID",
+                        "type": "string",
+                        "_special": "select_agent_runner_provider:dashscope",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "dashscope",
+                            "provider_settings.enable": True,
+                        },
+                    },
+                },
+            },
+            "ai": {
+                "description": "模型",
+                "hint": "当使用非内置 Agent 执行器时，默认聊天模型和默认图片转述模型可能会无效，但某些插件会依赖此配置项来调用 AI 能力。",
+                "type": "object",
+                "items": {
                    "provider_settings.default_provider_id": {
                        "description": "默认聊天模型",
                        "type": "string",
                        "_special": "select_provider",
-                        "hint": "留空时使用第一个模型。",
+                        "hint": "留空时使用第一个模型",
                    },
                    "provider_settings.default_image_caption_provider_id": {
                        "description": "默认图片转述模型",
                        "type": "string",
                        "_special": "select_provider",
-                        "hint": "留空代表不使用。可用于不支持视觉模态的聊天模型。",
+                        "hint": "留空代表不使用，可用于非多模态模型",
                    },
                    "provider_stt_settings.enable": {
                        "description": "启用语音转文本",
                        "type": "bool",
-                        "hint": "STT 总开关。",
+                        "hint": "STT 总开关",
                    },
                    "provider_stt_settings.provider_id": {
                        "description": "默认语音转文本模型",
@@ -2152,22 +2413,32 @@ CONFIG_METADATA_3 = {
                    "provider_tts_settings.enable": {
                        "description": "启用文本转语音",
                        "type": "bool",
-                        "hint": "TTS 总开关。当关闭时，会话启用 TTS 也不会生效。",
+                        "hint": "TTS 总开关",
                    },
                    "provider_tts_settings.provider_id": {
                        "description": "默认文本转语音模型",
                        "type": "string",
-                        "hint": "用户也可使用 /provider 单独选择会话的 TTS 模型。",
                        "_special": "select_provider_tts",
                        "condition": {
                            "provider_tts_settings.enable": True,
                        },
                    },
+                    "provider_tts_settings.trigger_probability": {
+                        "description": "TTS 触发概率",
+                        "type": "float",
+                        "slider": {"min": 0, "max": 1, "step": 0.05},
+                        "condition": {
+                            "provider_tts_settings.enable": True,
+                        },
+                    },
                    "provider_settings.image_caption_prompt": {
                        "description": "图片转述提示词",
                        "type": "text",
                    },
                },
+                "condition": {
+                    "provider_settings.enable": True,
+                },
            },
            "persona": {
                "description": "人格",
@@ -2179,6 +2450,10 @@ CONFIG_METADATA_3 = {
                        "_special": "select_persona",
                    },
                },
+                "condition": {
+                    "provider_settings.agent_runner_type": "local",
+                    "provider_settings.enable": True,
+                },
            },
            "knowledgebase": {
                "description": "知识库",
@@ -2201,6 +2476,15 @@ CONFIG_METADATA_3 = {
                        "type": "int",
                        "hint": "从知识库中检索到的结果数量，越大可能获得越多相关信息，但也可能引入噪音。建议根据实际需求调整",
                    },
+                    "kb_agentic_mode": {
+                        "description": "Agentic 知识库检索",
+                        "type": "bool",
+                        "hint": "启用后，知识库检索将作为 LLM Tool，由模型自主决定何时调用知识库进行查询。需要模型支持函数调用能力。",
+                    },
+                },
+                "condition": {
+                    "provider_settings.agent_runner_type": "local",
+                    "provider_settings.enable": True,
                },
            },
            "websearch": {
@@ -2238,7 +2522,41 @@ CONFIG_METADATA_3 = {
                        "type": "bool",
                    },
                },
+                "condition": {
+                    "provider_settings.agent_runner_type": "local",
+                    "provider_settings.enable": True,
+                },
            },
+            # "file_extract": {
+            #     "description": "文档解析能力 [beta]",
+            #     "type": "object",
+            #     "items": {
+            #         "provider_settings.file_extract.enable": {
+            #             "description": "启用文档解析能力",
+            #             "type": "bool",
+            #         },
+            #         "provider_settings.file_extract.provider": {
+            #             "description": "文档解析提供商",
+            #             "type": "string",
+            #             "options": ["moonshotai"],
+            #             "condition": {
+            #                 "provider_settings.file_extract.enable": True,
+            #             },
+            #         },
+            #         "provider_settings.file_extract.moonshotai_api_key": {
+            #             "description": "Moonshot AI API Key",
+            #             "type": "string",
+            #             "condition": {
+            #                 "provider_settings.file_extract.provider": "moonshotai",
+            #                 "provider_settings.file_extract.enable": True,
+            #             },
+            #         },
+            #     },
+            #     "condition": {
+            #         "provider_settings.agent_runner_type": "local",
+            #         "provider_settings.enable": True,
+            #     },
+            # },
            "others": {
                "description": "其他配置",
                "type": "object",
@@ -2246,54 +2564,83 @@ CONFIG_METADATA_3 = {
                    "provider_settings.display_reasoning_text": {
                        "description": "显示思考内容",
                        "type": "bool",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.identifier": {
                        "description": "用户识别",
                        "type": "bool",
+                        "hint": "启用后，会在提示词前包含用户 ID 信息。",
                    },
                    "provider_settings.group_name_display": {
                        "description": "显示群名称",
                        "type": "bool",
-                        "hint": "启用后，在支持的平台(aiocqhttp)上会在 prompt 中包含群名称信息。",
+                        "hint": "启用后，在支持的平台(OneBot v11)上会在提示词前包含群名称信息。",
                    },
                    "provider_settings.datetime_system_prompt": {
                        "description": "现实世界时间感知",
                        "type": "bool",
+                        "hint": "启用后，会在系统提示词中附带当前时间信息。",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.show_tool_use_status": {
                        "description": "输出函数调用状态",
                        "type": "bool",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.max_agent_step": {
                        "description": "工具调用轮数上限",
                        "type": "int",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.tool_call_timeout": {
                        "description": "工具调用超时时间（秒）",
                        "type": "int",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.streaming_response": {
-                        "description": "流式回复",
+                        "description": "流式输出",
                        "type": "bool",
                    },
-                    "provider_settings.streaming_segmented": {
-                        "description": "不支持流式回复的平台采取分段输出",
-                        "type": "bool",
+                    "provider_settings.unsupported_streaming_strategy": {
+                        "description": "不支持流式回复的平台",
+                        "type": "string",
+                        "options": ["realtime_segmenting", "turn_off"],
+                        "hint": "选择在不支持流式回复的平台上的处理方式。实时分段回复会在系统接收流式响应检测到诸如标点符号等分段点时，立即发送当前已接收的内容",
+                        "labels": ["实时分段回复", "关闭流式回复"],
+                        "condition": {
+                            "provider_settings.streaming_response": True,
+                        },
                    },
                    "provider_settings.max_context_length": {
                        "description": "最多携带对话轮数",
                        "type": "int",
-                        "hint": "超出这个数量时丢弃最旧的部分，一轮聊天记为 1 条。-1 为不限制。",
+                        "hint": "超出这个数量时丢弃最旧的部分，一轮聊天记为 1 条，-1 为不限制",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.dequeue_context_length": {
                        "description": "丢弃对话轮数",
                        "type": "int",
-                        "hint": "超出最多携带对话轮数时, 一次丢弃的聊天轮数。",
+                        "hint": "超出最多携带对话轮数时, 一次丢弃的聊天轮数",
+                        "condition": {
+                            "provider_settings.agent_runner_type": "local",
+                        },
                    },
                    "provider_settings.wake_prefix": {
                        "description": "LLM 聊天额外唤醒前缀 ",
                        "type": "string",
-                        "hint": "如果唤醒前缀为 `/`, 额外聊天唤醒前缀为 `chat`，则需要 `/chat` 才会触发 LLM 请求。默认为空。",
+                        "hint": "如果唤醒前缀为 /, 额外聊天唤醒前缀为 chat，则需要 /chat 才会触发 LLM 请求",
                    },
                    "provider_settings.prompt_prefix": {
                        "description": "用户提示词",
@@ -2304,6 +2651,14 @@ CONFIG_METADATA_3 = {
                        "description": "开启 TTS 时同时输出语音和文字内容",
                        "type": "bool",
                    },
+                    "provider_settings.reachability_check": {
+                        "description": "提供商可达性检测",
+                        "type": "bool",
+                        "hint": "/provider 命令列出模型时是否并发检测连通性。开启后会主动调用模型测试连通性，可能产生额外 token 消耗。",
+                    },
+                },
+                "condition": {
+                    "provider_settings.enable": True,
                },
            },
        },
@@ -2354,6 +2709,11 @@ CONFIG_METADATA_3 = {
                        "description": "只 @ 机器人是否触发等待",
                        "type": "bool",
                    },
+                    "disable_builtin_commands": {
+                        "description": "禁用自带指令",
+                        "type": "bool",
+                        "hint": "禁用所有 AstrBot 的自带指令，如 help, provider, model 等。",
+                    },
                },
            },
            "whitelist": {
@@ -2568,9 +2928,26 @@ CONFIG_METADATA_3 = {
                        "description": "分段回复字数阈值",
                        "type": "int",
                    },
+                    "platform_settings.segmented_reply.split_mode": {
+                        "description": "分段模式",
+                        "type": "string",
+                        "options": ["regex", "words"],
+                        "labels": ["正则表达式", "分段词列表"],
+                    },
                    "platform_settings.segmented_reply.regex": {
                        "description": "分段正则表达式",
                        "type": "string",
+                        "condition": {
+                            "platform_settings.segmented_reply.split_mode": "regex",
+                        },
+                    },
+                    "platform_settings.segmented_reply.split_words": {
+                        "description": "分段词列表",
+                        "type": "list",
+                        "hint": "检测到列表中的任意词时进行分段，如：。、？、！等",
+                        "condition": {
+                            "platform_settings.segmented_reply.split_mode": "words",
+                        },
                    },
                    "platform_settings.segmented_reply.content_cleanup_rule": {
                        "description": "内容过滤正则表达式",
@@ -2594,7 +2971,16 @@ CONFIG_METADATA_3 = {
                    "provider_ltm_settings.image_caption": {
                        "description": "自动理解图片",
                        "type": "bool",
-                        "hint": "需要设置默认图片转述模型。",
+                        "hint": "需要设置群聊图片转述模型。",
+                    },
+                    "provider_ltm_settings.image_caption_provider_id": {
+                        "description": "群聊图片转述模型",
+                        "type": "string",
+                        "_special": "select_provider",
+                        "hint": "用于群聊上下文感知的图片理解，与默认图片转述模型分开配置。",
+                        "condition": {
+                            "provider_ltm_settings.image_caption": True,
+                        },
                    },
                    "provider_ltm_settings.active_reply.enable": {
                        "description": "主动回复",
@@ -2612,6 +2998,7 @@ CONFIG_METADATA_3 = {
                        "description": "回复概率",
                        "type": "float",
                        "hint": "0.0-1.0 之间的数值",
+                        "slider": {"min": 0, "max": 1, "step": 0.05},
                        "condition": {
                            "provider_ltm_settings.active_reply.enable": True,
                        },
@@ -0,0 +1,111 @@
+"""
+配置元数据国际化工具
+
+提供配置元数据的国际化键转换功能
+"""
+
+from typing import Any
+
+
+class ConfigMetadataI18n:
+    """配置元数据国际化转换器"""
+
+    @staticmethod
+    def _get_i18n_key(group: str, section: str, field: str, attr: str) -> str:
+        """
+        生成国际化键
+
+        Args:
+            group: 配置组，如 'ai_group', 'platform_group'
+            section: 配置节，如 'agent_runner', 'general'
+            field: 字段名，如 'enable', 'default_provider'
+            attr: 属性类型，如 'description', 'hint', 'labels'
+
+        Returns:
+            国际化键，格式如: 'ai_group.agent_runner.enable.description'
+        """
+        if field:
+            return f"{group}.{section}.{field}.{attr}"
+        else:
+            return f"{group}.{section}.{attr}"
+
+    @staticmethod
+    def convert_to_i18n_keys(metadata: dict[str, Any]) -> dict[str, Any]:
+        """
+        将配置元数据转换为使用国际化键
+
+        Args:
+            metadata: 原始配置元数据字典
+
+        Returns:
+            使用国际化键的配置元数据字典
+        """
+        result = {}
+
+        for group_key, group_data in metadata.items():
+            group_result = {
+                "name": f"{group_key}.name",
+                "metadata": {},
+            }
+
+            for section_key, section_data in group_data.get("metadata", {}).items():
+                section_result = {
+                    "description": f"{group_key}.{section_key}.description",
+                    "type": section_data.get("type"),
+                }
+
+                # 复制其他属性
+                for key in ["items", "condition", "_special", "invisible"]:
+                    if key in section_data:
+                        section_result[key] = section_data[key]
+
+                # 处理 hint
+                if "hint" in section_data:
+                    section_result["hint"] = f"{group_key}.{section_key}.hint"
+
+                # 处理 items 中的字段
+                if "items" in section_data and isinstance(section_data["items"], dict):
+                    items_result = {}
+                    for field_key, field_data in section_data["items"].items():
+                        # 处理嵌套的点号字段名（如 provider_settings.enable）
+                        field_name = field_key
+
+                        field_result = {}
+
+                        # 复制基本属性
+                        for attr in [
+                            "type",
+                            "condition",
+                            "_special",
+                            "invisible",
+                            "options",
+                            "slider",
+                        ]:
+                            if attr in field_data:
+                                field_result[attr] = field_data[attr]
+
+                        # 转换文本属性为国际化键
+                        if "description" in field_data:
+                            field_result["description"] = (
+                                f"{group_key}.{section_key}.{field_name}.description"
+                            )
+
+                        if "hint" in field_data:
+                            field_result["hint"] = (
+                                f"{group_key}.{section_key}.{field_name}.hint"
+                            )
+
+                        if "labels" in field_data:
+                            field_result["labels"] = (
+                                f"{group_key}.{section_key}.{field_name}.labels"
+                            )
+
+                        items_result[field_key] = field_result
+
+                    section_result["items"] = items_result
+
+                group_result["metadata"][section_key] = section_result
+
+            result[group_key] = group_result
+
+        return result
@@ -16,12 +16,12 @@ import time
 import traceback
 from asyncio import Queue

-from astrbot.core import LogBroker, logger, sp
+from astrbot.api import logger, sp
+from astrbot.core import LogBroker
 from astrbot.core.astrbot_config_mgr import AstrBotConfigManager
 from astrbot.core.config.default import VERSION
 from astrbot.core.conversation_mgr import ConversationManager
 from astrbot.core.db import BaseDatabase
-from astrbot.core.db.migration.migra_45_to_46 import migrate_45_to_46
 from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
 from astrbot.core.persona_mgr import PersonaManager
 from astrbot.core.pipeline.scheduler import PipelineContext, PipelineScheduler
@@ -33,6 +33,7 @@ from astrbot.core.star.context import Context
 from astrbot.core.star.star_handler import EventType, star_handlers_registry, star_map
 from astrbot.core.umop_config_router import UmopConfigRouter
 from astrbot.core.updator import AstrBotUpdator
+from astrbot.core.utils.migra_helper import migra

 from . import astrbot_config, html_renderer
 from .event_bus import EventBus
@@ -96,11 +97,16 @@ class AstrBotCoreLifecycle:
            sp=sp,
        )

-        # 4.5 to 4.6 migration for umop_config_router
+        # apply migration
        try:
-            await migrate_45_to_46(self.astrbot_config_mgr, self.umop_config_router)
+            await migra(
+                self.db,
+                self.astrbot_config_mgr,
+                self.umop_config_router,
+                self.astrbot_config_mgr,
+            )
        except Exception as e:
-            logger.error(f"Migration from version 4.5 to 4.6 failed: {e!s}")
+            logger.error(f"AstrBot migration failed: {e!s}")
            logger.error(traceback.format_exc())

        # 初始化事件队列
@@ -191,7 +197,7 @@ class AstrBotCoreLifecycle:
        # 把插件中注册的所有协程函数注册到事件总线中并执行
        extra_tasks = []
        for task in self.star_context._register_tasks:
-            extra_tasks.append(asyncio.create_task(task, name=task.__name__))
+            extra_tasks.append(asyncio.create_task(task, name=task.__name__))  # type: ignore

        tasks_ = [event_bus_task, *extra_tasks]
        for task in tasks_:
@@ -5,14 +5,14 @@ from contextlib import asynccontextmanager
 from dataclasses import dataclass

 from deprecated import deprecated
-from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
-from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine

 from astrbot.core.db.po import (
    Attachment,
    ConversationV2,
    Persona,
    PlatformMessageHistory,
+    PlatformSession,
    PlatformStat,
    Preference,
    Stats,
@@ -31,7 +31,7 @@ class BaseDatabase(abc.ABC):
            echo=False,
            future=True,
        )
-        self.AsyncSessionLocal = sessionmaker(
+        self.AsyncSessionLocal = async_sessionmaker(
            self.engine,
            class_=AsyncSession,
            expire_on_commit=False,
@@ -172,7 +172,7 @@ class BaseDatabase(abc.ABC):
        content: dict,
        sender_id: str | None = None,
        sender_name: str | None = None,
-    ) -> None:
+    ) -> PlatformMessageHistory:
        """Insert a new platform message history record."""
        ...

@@ -183,7 +183,7 @@ class BaseDatabase(abc.ABC):
        user_id: str,
        offset_sec: int = 86400,
    ) -> None:
-        """Delete platform message history records older than the specified offset."""
+        """Delete platform message history records newer than the specified offset."""
        ...

    @abc.abstractmethod
@@ -197,6 +197,14 @@ class BaseDatabase(abc.ABC):
        """Get platform message history for a specific user."""
        ...

+    @abc.abstractmethod
+    async def get_platform_message_history_by_id(
+        self,
+        message_id: int,
+    ) -> PlatformMessageHistory | None:
+        """Get a platform message history record by its ID."""
+        ...
+
    @abc.abstractmethod
    async def insert_attachment(
        self,
@@ -212,6 +220,27 @@ class BaseDatabase(abc.ABC):
        """Get an attachment by its ID."""
        ...

+    @abc.abstractmethod
+    async def get_attachments(self, attachment_ids: list[str]) -> list[Attachment]:
+        """Get multiple attachments by their IDs."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_attachment(self, attachment_id: str) -> bool:
+        """Delete an attachment by its ID.
+
+        Returns True if the attachment was deleted, False if it was not found.
+        """
+        ...
+
+    @abc.abstractmethod
+    async def delete_attachments(self, attachment_ids: list[str]) -> int:
+        """Delete multiple attachments by their IDs.
+
+        Returns the number of attachments deleted.
+        """
+        ...
+
    @abc.abstractmethod
    async def insert_persona(
        self,
@@ -313,3 +342,51 @@ class BaseDatabase(abc.ABC):
    ) -> tuple[list[dict], int]:
        """Get paginated session conversations with joined conversation and persona details, support search and platform filter."""
        ...
+
+    # ====
+    # Platform Session Management
+    # ====
+
+    @abc.abstractmethod
+    async def create_platform_session(
+        self,
+        creator: str,
+        platform_id: str = "webchat",
+        session_id: str | None = None,
+        display_name: str | None = None,
+        is_group: int = 0,
+    ) -> PlatformSession:
+        """Create a new Platform session."""
+        ...
+
+    @abc.abstractmethod
+    async def get_platform_session_by_id(
+        self, session_id: str
+    ) -> PlatformSession | None:
+        """Get a Platform session by its ID."""
+        ...
+
+    @abc.abstractmethod
+    async def get_platform_sessions_by_creator(
+        self,
+        creator: str,
+        platform_id: str | None = None,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> list[PlatformSession]:
+        """Get all Platform sessions for a specific creator (username) and optionally platform."""
+        ...
+
+    @abc.abstractmethod
+    async def update_platform_session(
+        self,
+        session_id: str,
+        display_name: str | None = None,
+    ) -> None:
+        """Update a Platform session's updated_at timestamp and optionally display_name."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_platform_session(self, session_id: str) -> None:
+        """Delete a Platform session by its ID."""
+        ...
@@ -70,6 +70,7 @@ async def migration_conversation_table(
                        logger.info(
                            f"未找到该条旧会话对应的具体数据: {conversation}, 跳过。",
                        )
+                        continue
                    if ":" not in conv.user_id:
                        continue
                    session = MessageSesion.from_str(session_str=conv.user_id)
@@ -207,6 +208,7 @@ async def migration_webchat_data(
                        logger.info(
                            f"未找到该条旧会话对应的具体数据: {conversation}, 跳过。",
                        )
+                        continue
                    if ":" in conv.user_id:
                        continue
                    platform_id = "webchat"
@@ -0,0 +1,131 @@
+"""Migration script for WebChat sessions.
+
+This migration creates PlatformSession from existing platform_message_history records.
+
+Changes:
+- Creates platform_sessions table
+- Adds platform_id field (default: 'webchat')
+- Adds display_name field
+- Session_id format: {platform_id}_{uuid}
+"""
+
+from sqlalchemy import func, select
+from sqlmodel import col
+
+from astrbot.api import logger, sp
+from astrbot.core.db import BaseDatabase
+from astrbot.core.db.po import ConversationV2, PlatformMessageHistory, PlatformSession
+
+
+async def migrate_webchat_session(db_helper: BaseDatabase):
+    """Create PlatformSession records from platform_message_history.
+
+    This migration extracts all unique user_ids from platform_message_history
+    where platform_id='webchat' and creates corresponding PlatformSession records.
+    """
+    # 检查是否已经完成迁移
+    migration_done = await db_helper.get_preference(
+        "global", "global", "migration_done_webchat_session_1"
+    )
+    if migration_done:
+        return
+
+    logger.info("开始执行数据库迁移（WebChat 会话迁移）...")
+
+    try:
+        async with db_helper.get_db() as session:
+            # 从 platform_message_history 创建 PlatformSession
+            query = (
+                select(
+                    col(PlatformMessageHistory.user_id),
+                    col(PlatformMessageHistory.sender_name),
+                    func.min(PlatformMessageHistory.created_at).label("earliest"),
+                    func.max(PlatformMessageHistory.updated_at).label("latest"),
+                )
+                .where(col(PlatformMessageHistory.platform_id) == "webchat")
+                .where(col(PlatformMessageHistory.sender_id) != "bot")
+                .group_by(col(PlatformMessageHistory.user_id))
+            )
+
+            result = await session.execute(query)
+            webchat_users = result.all()
+
+            if not webchat_users:
+                logger.info("没有找到需要迁移的 WebChat 数据")
+                await sp.put_async(
+                    "global", "global", "migration_done_webchat_session_1", True
+                )
+                return
+
+            logger.info(f"找到 {len(webchat_users)} 个 WebChat 会话需要迁移")
+
+            # 检查已存在的会话
+            existing_query = select(col(PlatformSession.session_id))
+            existing_result = await session.execute(existing_query)
+            existing_session_ids = {row[0] for row in existing_result.fetchall()}
+
+            # 查询 Conversations 表中的 title，用于设置 display_name
+            # 对于每个 user_id，对应的 conversation user_id 格式为: webchat:FriendMessage:webchat!astrbot!{user_id}
+            user_ids_to_query = [
+                f"webchat:FriendMessage:webchat!astrbot!{user_id}"
+                for user_id, _, _, _ in webchat_users
+            ]
+            conv_query = select(
+                col(ConversationV2.user_id), col(ConversationV2.title)
+            ).where(col(ConversationV2.user_id).in_(user_ids_to_query))
+            conv_result = await session.execute(conv_query)
+            # 创建 user_id -> title 的映射字典
+            title_map = {
+                user_id.replace("webchat:FriendMessage:webchat!astrbot!", ""): title
+                for user_id, title in conv_result.fetchall()
+            }
+
+            # 批量创建 PlatformSession 记录
+            sessions_to_add = []
+            skipped_count = 0
+
+            for user_id, sender_name, created_at, updated_at in webchat_users:
+                # user_id 就是 webchat_conv_id (session_id)
+                session_id = user_id
+
+                # sender_name 通常是 username，但可能为 None
+                creator = sender_name if sender_name else "guest"
+
+                # 检查是否已经存在该会话
+                if session_id in existing_session_ids:
+                    logger.debug(f"会话 {session_id} 已存在，跳过")
+                    skipped_count += 1
+                    continue
+
+                # 从 Conversations 表中获取 display_name
+                display_name = title_map.get(user_id)
+
+                # 创建新的 PlatformSession（保留原有的时间戳）
+                new_session = PlatformSession(
+                    session_id=session_id,
+                    platform_id="webchat",
+                    creator=creator,
+                    is_group=0,
+                    created_at=created_at,
+                    updated_at=updated_at,
+                    display_name=display_name,
+                )
+                sessions_to_add.append(new_session)
+
+            # 批量插入
+            if sessions_to_add:
+                session.add_all(sessions_to_add)
+                await session.commit()
+
+                logger.info(
+                    f"WebChat 会话迁移完成！成功迁移: {len(sessions_to_add)}, 跳过: {skipped_count}",
+                )
+            else:
+                logger.info("没有新会话需要迁移")
+
+        # 标记迁移完成
+        await sp.put_async("global", "global", "migration_done_webchat_session_1", True)
+
+    except Exception as e:
+        logger.error(f"迁移过程中发生错误: {e}", exc_info=True)
+        raise
@@ -127,7 +127,7 @@ class SQLiteDatabase:
        conn.text_factory = str
        return conn

-    def _exec_sql(self, sql: str, params: tuple = None):
+    def _exec_sql(self, sql: str, params: tuple | None = None):
        conn = self.conn
        try:
            c = self.conn.cursor()
@@ -224,9 +224,11 @@ class SQLiteDatabase:

        c.close()

-        return Stats(platform, [], [])
+        return Stats(platform)

-    def get_conversation_by_user_id(self, user_id: str, cid: str) -> Conversation:
+    def get_conversation_by_user_id(
+        self, user_id: str, cid: str
+    ) -> Conversation | None:
        try:
            c = self.conn.cursor()
        except sqlite3.ProgrammingError:
@@ -258,7 +260,7 @@ class SQLiteDatabase:
            (user_id, cid, history, updated_at, created_at),
        )

-    def get_conversations(self, user_id: str) -> tuple:
+    def get_conversations(self, user_id: str) -> list[Conversation]:
        try:
            c = self.conn.cursor()
        except sqlite3.ProgrammingError:
@@ -3,13 +3,7 @@ from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import TypedDict

-from sqlmodel import (
-    JSON,
-    Field,
-    SQLModel,
-    Text,
-    UniqueConstraint,
-)
+from sqlmodel import JSON, Field, SQLModel, Text, UniqueConstraint


 class PlatformStat(SQLModel, table=True):
@@ -18,7 +12,7 @@ class PlatformStat(SQLModel, table=True):
    Note: In astrbot v4, we moved `platform` table to here.
    """

-    __tablename__ = "platform_stats"
+    __tablename__: str = "platform_stats"

    id: int = Field(primary_key=True, sa_column_kwargs={"autoincrement": True})
    timestamp: datetime = Field(nullable=False)
@@ -37,9 +31,10 @@ class PlatformStat(SQLModel, table=True):


 class ConversationV2(SQLModel, table=True):
-    __tablename__ = "conversations"
+    __tablename__: str = "conversations"

-    inner_conversation_id: int = Field(
+    inner_conversation_id: int | None = Field(
+        default=None,
        primary_key=True,
        sa_column_kwargs={"autoincrement": True},
    )
@@ -74,7 +69,7 @@ class Persona(SQLModel, table=True):
    It can be used to customize the behavior of LLMs.
    """

-    __tablename__ = "personas"
+    __tablename__: str = "personas"

    id: int | None = Field(
        primary_key=True,
@@ -104,7 +99,7 @@ class Persona(SQLModel, table=True):
 class Preference(SQLModel, table=True):
    """This class represents preferences for bots."""

-    __tablename__ = "preferences"
+    __tablename__: str = "preferences"

    id: int | None = Field(
        default=None,
@@ -140,7 +135,7 @@ class PlatformMessageHistory(SQLModel, table=True):
    or platform-specific messages.
    """

-    __tablename__ = "platform_message_history"
+    __tablename__: str = "platform_message_history"

    id: int | None = Field(
        primary_key=True,
@@ -161,13 +156,55 @@ class PlatformMessageHistory(SQLModel, table=True):
    )


+class PlatformSession(SQLModel, table=True):
+    """Platform session table for managing user sessions across different platforms.
+
+    A session represents a chat window for a specific user on a specific platform.
+    Each session can have multiple conversations (对话) associated with it.
+    """
+
+    __tablename__: str = "platform_sessions"
+
+    inner_id: int | None = Field(
+        primary_key=True,
+        sa_column_kwargs={"autoincrement": True},
+        default=None,
+    )
+    session_id: str = Field(
+        max_length=100,
+        nullable=False,
+        unique=True,
+        default_factory=lambda: str(uuid.uuid4()),
+    )
+    platform_id: str = Field(default="webchat", nullable=False)
+    """Platform identifier (e.g., 'webchat', 'qq', 'discord')"""
+    creator: str = Field(nullable=False)
+    """Username of the session creator"""
+    display_name: str | None = Field(default=None, max_length=255)
+    """Display name for the session"""
+    is_group: int = Field(default=0, nullable=False)
+    """0 for private chat, 1 for group chat (not implemented yet)"""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        sa_column_kwargs={"onupdate": datetime.now(timezone.utc)},
+    )
+
+    __table_args__ = (
+        UniqueConstraint(
+            "session_id",
+            name="uix_platform_session_id",
+        ),
+    )
+
+
 class Attachment(SQLModel, table=True):
    """This class represents attachments for messages in AstrBot.

    Attachments can be images, files, or other media types.
    """

-    __tablename__ = "attachments"
+    __tablename__: str = "attachments"

    inner_attachment_id: int | None = Field(
        primary_key=True,
@@ -225,17 +262,17 @@ class Personality(TypedDict):
    在 v4.0.0 版本及之后，推荐使用上面的 Persona 类。并且， mood_imitation_dialogs 字段已被废弃。
    """

-    prompt: str = ""
-    name: str = ""
-    begin_dialogs: list[str] = []
-    mood_imitation_dialogs: list[str] = []
+    prompt: str
+    name: str
+    begin_dialogs: list[str]
+    mood_imitation_dialogs: list[str]
    """情感模拟对话预设。在 v4.0.0 版本及之后，已被废弃。"""
-    tools: list[str] | None = None
+    tools: list[str] | None
    """工具列表。None 表示使用所有工具，空列表表示不使用任何工具"""

    # cache
-    _begin_dialogs_processed: list[dict] = []
-    _mood_imitation_dialogs_processed: str = ""
+    _begin_dialogs_processed: list[dict]
+    _mood_imitation_dialogs_processed: str


 # ====
@@ -1,8 +1,9 @@
 import asyncio
 import threading
 import typing as T
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone

+from sqlalchemy import CursorResult
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlmodel import col, delete, desc, func, or_, select, text, update

@@ -12,6 +13,7 @@ from astrbot.core.db.po import (
    ConversationV2,
    Persona,
    PlatformMessageHistory,
+    PlatformSession,
    PlatformStat,
    Preference,
    SQLModel,
@@ -104,8 +106,8 @@ class SQLiteDatabase(BaseDatabase):
                text("""
                SELECT * FROM platform_stats
                WHERE timestamp >= :start_time
-                ORDER BY timestamp DESC
                GROUP BY platform_id
+                ORDER BY timestamp DESC
                """),
                {"start_time": start_time},
            )
@@ -412,7 +414,7 @@ class SQLiteDatabase(BaseDatabase):
        user_id,
        offset_sec=86400,
    ):
-        """Delete platform message history records older than the specified offset."""
+        """Delete platform message history records newer than the specified offset."""
        async with self.get_db() as session:
            session: AsyncSession
            async with session.begin():
@@ -422,7 +424,7 @@ class SQLiteDatabase(BaseDatabase):
                    delete(PlatformMessageHistory).where(
                        col(PlatformMessageHistory.platform_id) == platform_id,
                        col(PlatformMessageHistory.user_id) == user_id,
-                        col(PlatformMessageHistory.created_at) < cutoff_time,
+                        col(PlatformMessageHistory.created_at) >= cutoff_time,
                    ),
                )

@@ -448,6 +450,18 @@ class SQLiteDatabase(BaseDatabase):
            result = await session.execute(query.offset(offset).limit(page_size))
            return result.scalars().all()

+    async def get_platform_message_history_by_id(
+        self, message_id: int
+    ) -> PlatformMessageHistory | None:
+        """Get a platform message history record by its ID."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            query = select(PlatformMessageHistory).where(
+                PlatformMessageHistory.id == message_id
+            )
+            result = await session.execute(query)
+            return result.scalar_one_or_none()
+
    async def insert_attachment(self, path, type, mime_type):
        """Insert a new attachment record."""
        async with self.get_db() as session:
@@ -469,6 +483,48 @@ class SQLiteDatabase(BaseDatabase):
            result = await session.execute(query)
            return result.scalar_one_or_none()

+    async def get_attachments(self, attachment_ids: list[str]) -> list:
+        """Get multiple attachments by their IDs."""
+        if not attachment_ids:
+            return []
+        async with self.get_db() as session:
+            session: AsyncSession
+            query = select(Attachment).where(
+                col(Attachment.attachment_id).in_(attachment_ids)
+            )
+            result = await session.execute(query)
+            return list(result.scalars().all())
+
+    async def delete_attachment(self, attachment_id: str) -> bool:
+        """Delete an attachment by its ID.
+
+        Returns True if the attachment was deleted, False if it was not found.
+        """
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                query = delete(Attachment).where(
+                    col(Attachment.attachment_id) == attachment_id
+                )
+                result = T.cast(CursorResult, await session.execute(query))
+                return result.rowcount > 0
+
+    async def delete_attachments(self, attachment_ids: list[str]) -> int:
+        """Delete multiple attachments by their IDs.
+
+        Returns the number of attachments deleted.
+        """
+        if not attachment_ids:
+            return 0
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                query = delete(Attachment).where(
+                    col(Attachment.attachment_id).in_(attachment_ids)
+                )
+                result = T.cast(CursorResult, await session.execute(query))
+                return result.rowcount
+
    async def insert_persona(
        self,
        persona_id,
@@ -709,3 +765,101 @@ class SQLiteDatabase(BaseDatabase):
        t.start()
        t.join()
        return result
+
+    # ====
+    # Platform Session Management
+    # ====
+
+    async def create_platform_session(
+        self,
+        creator: str,
+        platform_id: str = "webchat",
+        session_id: str | None = None,
+        display_name: str | None = None,
+        is_group: int = 0,
+    ) -> PlatformSession:
+        """Create a new Platform session."""
+        kwargs = {}
+        if session_id:
+            kwargs["session_id"] = session_id
+
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                new_session = PlatformSession(
+                    creator=creator,
+                    platform_id=platform_id,
+                    display_name=display_name,
+                    is_group=is_group,
+                    **kwargs,
+                )
+                session.add(new_session)
+                await session.flush()
+                await session.refresh(new_session)
+                return new_session
+
+    async def get_platform_session_by_id(
+        self, session_id: str
+    ) -> PlatformSession | None:
+        """Get a Platform session by its ID."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            query = select(PlatformSession).where(
+                PlatformSession.session_id == session_id,
+            )
+            result = await session.execute(query)
+            return result.scalar_one_or_none()
+
+    async def get_platform_sessions_by_creator(
+        self,
+        creator: str,
+        platform_id: str | None = None,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> list[PlatformSession]:
+        """Get all Platform sessions for a specific creator (username) and optionally platform."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            offset = (page - 1) * page_size
+            query = select(PlatformSession).where(PlatformSession.creator == creator)
+
+            if platform_id:
+                query = query.where(PlatformSession.platform_id == platform_id)
+
+            query = (
+                query.order_by(desc(PlatformSession.updated_at))
+                .offset(offset)
+                .limit(page_size)
+            )
+            result = await session.execute(query)
+            return list(result.scalars().all())
+
+    async def update_platform_session(
+        self,
+        session_id: str,
+        display_name: str | None = None,
+    ) -> None:
+        """Update a Platform session's updated_at timestamp and optionally display_name."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                values: dict[str, T.Any] = {"updated_at": datetime.now(timezone.utc)}
+                if display_name is not None:
+                    values["display_name"] = display_name
+
+                await session.execute(
+                    update(PlatformSession)
+                    .where(col(PlatformSession.session_id) == session_id)
+                    .values(**values),
+                )
+
+    async def delete_platform_session(self, session_id: str) -> None:
+        """Delete a Platform session by its ID."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                await session.execute(
+                    delete(PlatformSession).where(
+                        col(PlatformSession.session_id) == session_id,
+                    ),
+                )
@@ -90,4 +90,6 @@ class EmbeddingStorage:
            path (str): 保存索引的路径

        """
+        if self.index is None:
+            return
        faiss.write_index(self.index, self.path)
@@ -27,7 +27,7 @@ class EventBus:
        self,
        event_queue: Queue,
        pipeline_scheduler_mapping: dict[str, PipelineScheduler],
-        astrbot_config_mgr: AstrBotConfigManager = None,
+        astrbot_config_mgr: AstrBotConfigManager,
    ):
        self.event_queue = event_queue  # 事件队列
        # abconf uuid -> scheduler
@@ -40,6 +40,11 @@ class EventBus:
            conf_info = self.astrbot_config_mgr.get_conf_info(event.unified_msg_origin)
            self._print_event(event, conf_info["name"])
            scheduler = self.pipeline_scheduler_mapping.get(conf_info["id"])
+            if not scheduler:
+                logger.error(
+                    f"PipelineScheduler not found for id: {conf_info['id']}, event ignored."
+                )
+                continue
            asyncio.create_task(scheduler.execute(event))

    def _print_event(self, event: AstrMessageEvent, conf_name: str):
@@ -0,0 +1,9 @@
+from __future__ import annotations
+
+
+class AstrBotError(Exception):
+    """Base exception for all AstrBot errors."""
+
+
+class ProviderNotFoundError(AstrBotError):
+    """Raised when a specified provider is not found."""
@@ -1,4 +1,7 @@
+import asyncio
 import json
+import re
+import time
 import uuid
 from pathlib import Path

@@ -8,12 +11,98 @@ from astrbot.core import logger
 from astrbot.core.db.vec_db.base import BaseVecDB
 from astrbot.core.db.vec_db.faiss_impl.vec_db import FaissVecDB
 from astrbot.core.provider.manager import ProviderManager
-from astrbot.core.provider.provider import EmbeddingProvider, RerankProvider
+from astrbot.core.provider.provider import (
+    EmbeddingProvider,
+    RerankProvider,
+)
+from astrbot.core.provider.provider import (
+    Provider as LLMProvider,
+)

 from .chunking.base import BaseChunker
+from .chunking.recursive import RecursiveCharacterChunker
 from .kb_db_sqlite import KBSQLiteDatabase
 from .models import KBDocument, KBMedia, KnowledgeBase
+from .parsers.url_parser import extract_text_from_url
 from .parsers.util import select_parser
+from .prompts import TEXT_REPAIR_SYSTEM_PROMPT
+
+
+class RateLimiter:
+    """一个简单的速率限制器"""
+
+    def __init__(self, max_rpm: int):
+        self.max_per_minute = max_rpm
+        self.interval = 60.0 / max_rpm if max_rpm > 0 else 0
+        self.last_call_time = 0
+
+    async def __aenter__(self):
+        if self.interval == 0:
+            return
+
+        now = time.monotonic()
+        elapsed = now - self.last_call_time
+
+        if elapsed < self.interval:
+            await asyncio.sleep(self.interval - elapsed)
+
+        self.last_call_time = time.monotonic()
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        pass
+
+
+async def _repair_and_translate_chunk_with_retry(
+    chunk: str,
+    repair_llm_service: LLMProvider,
+    rate_limiter: RateLimiter,
+    max_retries: int = 2,
+) -> list[str]:
+    """
+    Repairs, translates, and optionally re-chunks a single text chunk using the small LLM, with rate limiting.
+    """
+    # 为了防止 LLM 上下文污染，在 user_prompt 中也加入明确的指令
+    user_prompt = f"""IGNORE ALL PREVIOUS INSTRUCTIONS. Your ONLY task is to process the following text chunk according to the system prompt provided.
+
+Text chunk to process:
+---
+{chunk}
+---
+"""
+    for attempt in range(max_retries + 1):
+        try:
+            async with rate_limiter:
+                response = await repair_llm_service.text_chat(
+                    prompt=user_prompt, system_prompt=TEXT_REPAIR_SYSTEM_PROMPT
+                )
+
+            llm_output = response.completion_text
+
+            if "<discard_chunk />" in llm_output:
+                return []  # Signal to discard this chunk
+
+            # More robust regex to handle potential LLM formatting errors (spaces, newlines in tags)
+            matches = re.findall(
+                r"<\s*repaired_text\s*>\s*(.*?)\s*<\s*/\s*repaired_text\s*>",
+                llm_output,
+                re.DOTALL,
+            )
+
+            if matches:
+                # Further cleaning to ensure no empty strings are returned
+                return [m.strip() for m in matches if m.strip()]
+            else:
+                # If no valid tags and not explicitly discarded, discard it to be safe.
+                return []
+        except Exception as e:
+            logger.warning(
+                f"  - LLM call failed on attempt {attempt + 1}/{max_retries + 1}. Error: {str(e)}"
+            )
+
+    logger.error(
+        f"  - Failed to process chunk after {max_retries + 1} attempts. Using original text."
+    )
+    return [chunk]


 class KBHelper:
@@ -100,7 +189,7 @@ class KBHelper:
    async def upload_document(
        self,
        file_name: str,
-        file_content: bytes,
+        file_content: bytes | None,
        file_type: str,
        chunk_size: int = 512,
        chunk_overlap: int = 50,
@@ -108,6 +197,7 @@ class KBHelper:
        tasks_limit: int = 3,
        max_retries: int = 3,
        progress_callback=None,
+        pre_chunked_text: list[str] | None = None,
    ) -> KBDocument:
        """上传并处理文档（带原子性保证和失败清理）

@@ -130,46 +220,63 @@ class KBHelper:
        await self._ensure_vec_db()
        doc_id = str(uuid.uuid4())
        media_paths: list[Path] = []
+        file_size = 0

        # file_path = self.kb_files_dir / f"{doc_id}.{file_type}"
        # async with aiofiles.open(file_path, "wb") as f:
        #     await f.write(file_content)

        try:
-            # 阶段1: 解析文档
-            if progress_callback:
-                await progress_callback("parsing", 0, 100)
-
-            parser = await select_parser(f".{file_type}")
-            parse_result = await parser.parse(file_content, file_name)
-            text_content = parse_result.text
-            media_items = parse_result.media
-
-            if progress_callback:
-                await progress_callback("parsing", 100, 100)
-
-            # 保存媒体文件
+            chunks_text = []
            saved_media = []
-            for media_item in media_items:
-                media = await self._save_media(
-                    doc_id=doc_id,
-                    media_type=media_item.media_type,
-                    file_name=media_item.file_name,
-                    content=media_item.content,
-                    mime_type=media_item.mime_type,
+
+            if pre_chunked_text is not None:
+                # 如果提供了预分块文本，直接使用
+                chunks_text = pre_chunked_text
+                file_size = sum(len(chunk) for chunk in chunks_text)
+                logger.info(f"使用预分块文本进行上传，共 {len(chunks_text)} 个块。")
+            else:
+                # 否则，执行标准的文件解析和分块流程
+                if file_content is None:
+                    raise ValueError(
+                        "当未提供 pre_chunked_text 时，file_content 不能为空。"
+                    )
+
+                file_size = len(file_content)
+
+                # 阶段1: 解析文档
+                if progress_callback:
+                    await progress_callback("parsing", 0, 100)
+
+                parser = await select_parser(f".{file_type}")
+                parse_result = await parser.parse(file_content, file_name)
+                text_content = parse_result.text
+                media_items = parse_result.media
+
+                if progress_callback:
+                    await progress_callback("parsing", 100, 100)
+
+                # 保存媒体文件
+                for media_item in media_items:
+                    media = await self._save_media(
+                        doc_id=doc_id,
+                        media_type=media_item.media_type,
+                        file_name=media_item.file_name,
+                        content=media_item.content,
+                        mime_type=media_item.mime_type,
+                    )
+                    saved_media.append(media)
+                    media_paths.append(Path(media.file_path))
+
+                # 阶段2: 分块
+                if progress_callback:
+                    await progress_callback("chunking", 0, 100)
+
+                chunks_text = await self.chunker.chunk(
+                    text_content,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
                )
-                saved_media.append(media)
-                media_paths.append(Path(media.file_path))
-
-            # 阶段2: 分块
-            if progress_callback:
-                await progress_callback("chunking", 0, 100)
-
-            chunks_text = await self.chunker.chunk(
-                text_content,
-                chunk_size=chunk_size,
-                chunk_overlap=chunk_overlap,
-            )
            contents = []
            metadatas = []
            for idx, chunk_text in enumerate(chunks_text):
@@ -205,7 +312,7 @@ class KBHelper:
                kb_id=self.kb.kb_id,
                doc_name=file_name,
                file_type=file_type,
-                file_size=len(file_content),
+                file_size=file_size,
                # file_path=str(file_path),
                file_path="",
                chunk_count=len(chunks_text),
@@ -359,3 +466,177 @@ class KBHelper:
        )

        return media
+
+    async def upload_from_url(
+        self,
+        url: str,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+        batch_size: int = 32,
+        tasks_limit: int = 3,
+        max_retries: int = 3,
+        progress_callback=None,
+        enable_cleaning: bool = False,
+        cleaning_provider_id: str | None = None,
+    ) -> KBDocument:
+        """从 URL 上传并处理文档（带原子性保证和失败清理）
+        Args:
+            url: 要提取内容的网页 URL
+            chunk_size: 文本块大小
+            chunk_overlap: 文本块重叠大小
+            batch_size: 批处理大小
+            tasks_limit: 并发任务限制
+            max_retries: 最大重试次数
+            progress_callback: 进度回调函数，接收参数 (stage, current, total)
+                - stage: 当前阶段 ('extracting', 'cleaning', 'parsing', 'chunking', 'embedding')
+                - current: 当前进度
+                - total: 总数
+        Returns:
+            KBDocument: 上传的文档对象
+        Raises:
+            ValueError: 如果 URL 为空或无法提取内容
+            IOError: 如果网络请求失败
+        """
+        # 获取 Tavily API 密钥
+        config = self.prov_mgr.acm.default_conf
+        tavily_keys = config.get("provider_settings", {}).get(
+            "websearch_tavily_key", []
+        )
+        if not tavily_keys:
+            raise ValueError(
+                "Error: Tavily API key is not configured in provider_settings."
+            )
+
+        # 阶段1: 从 URL 提取内容
+        if progress_callback:
+            await progress_callback("extracting", 0, 100)
+
+        try:
+            text_content = await extract_text_from_url(url, tavily_keys)
+        except Exception as e:
+            logger.error(f"Failed to extract content from URL {url}: {e}")
+            raise OSError(f"Failed to extract content from URL {url}: {e}") from e
+
+        if not text_content:
+            raise ValueError(f"No content extracted from URL: {url}")
+
+        if progress_callback:
+            await progress_callback("extracting", 100, 100)
+
+        # 阶段2: (可选)清洗内容并分块
+        final_chunks = await self._clean_and_rechunk_content(
+            content=text_content,
+            url=url,
+            progress_callback=progress_callback,
+            enable_cleaning=enable_cleaning,
+            cleaning_provider_id=cleaning_provider_id,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        if enable_cleaning and not final_chunks:
+            raise ValueError(
+                "内容清洗后未提取到有效文本。请尝试关闭内容清洗功能，或更换更高性能的LLM模型后重试。"
+            )
+
+        # 创建一个虚拟文件名
+        file_name = url.split("/")[-1] or f"document_from_{url}"
+        if not Path(file_name).suffix:
+            file_name += ".url"
+
+        # 复用现有的 upload_document 方法，但传入预分块文本
+        return await self.upload_document(
+            file_name=file_name,
+            file_content=None,
+            file_type="url",  # 使用 'url' 作为特殊文件类型
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            batch_size=batch_size,
+            tasks_limit=tasks_limit,
+            max_retries=max_retries,
+            progress_callback=progress_callback,
+            pre_chunked_text=final_chunks,
+        )
+
+    async def _clean_and_rechunk_content(
+        self,
+        content: str,
+        url: str,
+        progress_callback=None,
+        enable_cleaning: bool = False,
+        cleaning_provider_id: str | None = None,
+        repair_max_rpm: int = 60,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+    ) -> list[str]:
+        """
+        对从 URL 获取的内容进行清洗、修复、翻译和重新分块。
+        """
+        if not enable_cleaning:
+            # 如果不启用清洗，则使用从前端传递的参数进行分块
+            logger.info(
+                f"内容清洗未启用，使用指定参数进行分块: chunk_size={chunk_size}, chunk_overlap={chunk_overlap}"
+            )
+            return await self.chunker.chunk(
+                content, chunk_size=chunk_size, chunk_overlap=chunk_overlap
+            )
+
+        if not cleaning_provider_id:
+            logger.warning(
+                "启用了内容清洗，但未提供 cleaning_provider_id，跳过清洗并使用默认分块。"
+            )
+            return await self.chunker.chunk(content)
+
+        if progress_callback:
+            await progress_callback("cleaning", 0, 100)
+
+        try:
+            # 获取指定的 LLM Provider
+            llm_provider = await self.prov_mgr.get_provider_by_id(cleaning_provider_id)
+            if not llm_provider or not isinstance(llm_provider, LLMProvider):
+                raise ValueError(
+                    f"无法找到 ID 为 {cleaning_provider_id} 的 LLM Provider 或类型不正确"
+                )
+
+            # 初步分块
+            # 优化分隔符，优先按段落分割，以获得更高质量的文本块
+            text_splitter = RecursiveCharacterChunker(
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+                separators=["\n\n", "\n", " "],  # 优先使用段落分隔符
+            )
+            initial_chunks = await text_splitter.chunk(content)
+            logger.info(f"初步分块完成，生成 {len(initial_chunks)} 个块用于修复。")
+
+            # 并发处理所有块
+            rate_limiter = RateLimiter(repair_max_rpm)
+            tasks = [
+                _repair_and_translate_chunk_with_retry(
+                    chunk, llm_provider, rate_limiter
+                )
+                for chunk in initial_chunks
+            ]
+
+            repaired_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+            final_chunks = []
+            for i, result in enumerate(repaired_results):
+                if isinstance(result, Exception):
+                    logger.warning(f"块 {i} 处理异常: {str(result)}. 回退到原始块。")
+                    final_chunks.append(initial_chunks[i])
+                elif isinstance(result, list):
+                    final_chunks.extend(result)
+
+            logger.info(
+                f"文本修复完成: {len(initial_chunks)} 个原始块 -> {len(final_chunks)} 个最终块。"
+            )
+
+            if progress_callback:
+                await progress_callback("cleaning", 100, 100)
+
+            return final_chunks
+
+        except Exception as e:
+            logger.error(f"使用 Provider '{cleaning_provider_id}' 清洗内容失败: {e}")
+            # 清洗失败，返回默认分块结果，保证流程不中断
+            return await self.chunker.chunk(content)
@@ -8,7 +8,7 @@ from astrbot.core.provider.manager import ProviderManager
 from .chunking.recursive import RecursiveCharacterChunker
 from .kb_db_sqlite import KBSQLiteDatabase
 from .kb_helper import KBHelper
-from .models import KnowledgeBase
+from .models import KBDocument, KnowledgeBase
 from .retrieval.manager import RetrievalManager, RetrievalResult
 from .retrieval.rank_fusion import RankFusion
 from .retrieval.sparse_retriever import SparseRetriever
@@ -284,3 +284,47 @@ class KnowledgeBaseManager:
                await self.kb_db.close()
            except Exception as e:
                logger.error(f"关闭知识库元数据数据库失败: {e}")
+
+    async def upload_from_url(
+        self,
+        kb_id: str,
+        url: str,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+        batch_size: int = 32,
+        tasks_limit: int = 3,
+        max_retries: int = 3,
+        progress_callback=None,
+    ) -> KBDocument:
+        """从 URL 上传文档到指定的知识库
+
+        Args:
+            kb_id: 知识库 ID
+            url: 要提取内容的网页 URL
+            chunk_size: 文本块大小
+            chunk_overlap: 文本块重叠大小
+            batch_size: 批处理大小
+            tasks_limit: 并发任务限制
+            max_retries: 最大重试次数
+            progress_callback: 进度回调函数
+
+        Returns:
+            KBDocument: 上传的文档对象
+
+        Raises:
+            ValueError: 如果知识库不存在或 URL 为空
+            IOError: 如果网络请求失败
+        """
+        kb_helper = await self.get_kb(kb_id)
+        if not kb_helper:
+            raise ValueError(f"Knowledge base with id {kb_id} not found.")
+
+        return await kb_helper.upload_from_url(
+            url=url,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            batch_size=batch_size,
+            tasks_limit=tasks_limit,
+            max_retries=max_retries,
+            progress_callback=progress_callback,
+        )
@@ -0,0 +1,103 @@
+import asyncio
+
+import aiohttp
+
+
+class URLExtractor:
+    """URL 内容提取器，封装了 Tavily API 调用和密钥管理"""
+
+    def __init__(self, tavily_keys: list[str]):
+        """
+        初始化 URL 提取器
+
+        Args:
+            tavily_keys: Tavily API 密钥列表
+        """
+        if not tavily_keys:
+            raise ValueError("Error: Tavily API keys are not configured.")
+
+        self.tavily_keys = tavily_keys
+        self.tavily_key_index = 0
+        self.tavily_key_lock = asyncio.Lock()
+
+    async def _get_tavily_key(self) -> str:
+        """并发安全的从列表中获取并轮换Tavily API密钥。"""
+        async with self.tavily_key_lock:
+            key = self.tavily_keys[self.tavily_key_index]
+            self.tavily_key_index = (self.tavily_key_index + 1) % len(self.tavily_keys)
+            return key
+
+    async def extract_text_from_url(self, url: str) -> str:
+        """
+        使用 Tavily API 从 URL 提取主要文本内容。
+        这是 web_searcher 插件中 tavily_extract_web_page 方法的简化版本，
+        专门为知识库模块设计，不依赖 AstrMessageEvent。
+
+        Args:
+            url: 要提取内容的网页 URL
+
+        Returns:
+            提取的文本内容
+
+        Raises:
+            ValueError: 如果 URL 为空或 API 密钥未配置
+            IOError: 如果请求失败或返回错误
+        """
+        if not url:
+            raise ValueError("Error: url must be a non-empty string.")
+
+        tavily_key = await self._get_tavily_key()
+        api_url = "https://api.tavily.com/extract"
+        headers = {
+            "Authorization": f"Bearer {tavily_key}",
+            "Content-Type": "application/json",
+        }
+
+        payload = {
+            "urls": [url],
+            "extract_depth": "basic",  # 使用基础提取深度
+        }
+
+        try:
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.post(
+                    api_url,
+                    json=payload,
+                    headers=headers,
+                    timeout=30.0,  # 增加超时时间，因为内容提取可能需要更长时间
+                ) as response:
+                    if response.status != 200:
+                        reason = await response.text()
+                        raise OSError(
+                            f"Tavily web extraction failed: {reason}, status: {response.status}"
+                        )
+
+                    data = await response.json()
+                    results = data.get("results", [])
+
+                    if not results:
+                        raise ValueError(f"No content extracted from URL: {url}")
+
+                    # 返回第一个结果的内容
+                    return results[0].get("raw_content", "")
+
+        except aiohttp.ClientError as e:
+            raise OSError(f"Failed to fetch URL {url}: {e}") from e
+        except Exception as e:
+            raise OSError(f"Failed to extract content from URL {url}: {e}") from e
+
+
+# 为了向后兼容，提供一个简单的函数接口
+async def extract_text_from_url(url: str, tavily_keys: list[str]) -> str:
+    """
+    简单的函数接口，用于从 URL 提取文本内容
+
+    Args:
+        url: 要提取内容的网页 URL
+        tavily_keys: Tavily API 密钥列表
+
+    Returns:
+        提取的文本内容
+    """
+    extractor = URLExtractor(tavily_keys)
+    return await extractor.extract_text_from_url(url)
@@ -0,0 +1,65 @@
+TEXT_REPAIR_SYSTEM_PROMPT = """You are a meticulous digital archivist. Your mission is to reconstruct a clean, readable article from raw, noisy text chunks.
+
+**Core Task:**
+1.  **Analyze:** Examine the text chunk to separate "signal" (substantive information) from "noise" (UI elements, ads, navigation, footers).
+2.  **Process:** Clean and repair the signal. **Do not translate it.** Keep the original language.
+
+**Crucial Rules:**
+- **NEVER discard a chunk if it contains ANY valuable information.** Your primary duty is to salvage content.
+- **If a chunk contains multiple distinct topics, split them.** Enclose each topic in its own `<repaired_text>` tag.
+- Your output MUST be ONLY `<repaired_text>...</repaired_text>` tags or a single `<discard_chunk />` tag.
+
+---
+**Example 1: Chunk with Noise and Signal**
+
+*Input Chunk:*
+"Home | About | Products | **The Llama is a domesticated South American camelid.** | © 2025 ACME Corp."
+
+*Your Thought Process:*
+1.  "Home | About | Products..." and "© 2025 ACME Corp." are noise.
+2.  "The Llama is a domesticated..." is the signal.
+3.  I must extract the signal and wrap it.
+
+*Your Output:*
+<repaired_text>
+The Llama is a domesticated South American camelid.
+</repaired_text>
+
+---
+**Example 2: Chunk with ONLY Noise**
+
+*Input Chunk:*
+"Next Page > | Subscribe to our newsletter | Follow us on X"
+
+*Your Thought Process:*
+1.  This entire chunk is noise. There is no signal.
+2.  I must discard this.
+
+*Your Output:*
+<discard_chunk />
+
+---
+**Example 3: Chunk with Multiple Topics (Requires Splitting)**
+
+*Input Chunk:*
+"## Chapter 1: The Sun
+The Sun is the star at the center of the Solar System.
+
+## Chapter 2: The Moon
+The Moon is Earth's only natural satellite."
+
+*Your Thought Process:*
+1.  This chunk contains two distinct topics.
+2.  I must process them separately to maintain semantic integrity.
+3.  I will create two `<repaired_text>` blocks.
+
+*Your Output:*
+<repaired_text>
+## Chapter 1: The Sun
+The Sun is the star at the center of the Solar System.
+</repaired_text>
+<repaired_text>
+## Chapter 2: The Moon
+The Moon is Earth's only natural satellite.
+</repaired_text>
+"""
@@ -166,7 +166,11 @@ class RetrievalManager:
        # 5. Rerank
        first_rerank = None
        for kb_id in kb_ids:
-            vec_db: FaissVecDB = kb_options[kb_id]["vec_db"]
+            vec_db = kb_options[kb_id]["vec_db"]
+            if not isinstance(vec_db, FaissVecDB):
+                logger.warning(f"vec_db for kb_id {kb_id} is not FaissVecDB")
+                continue
+
            rerank_pi = kb_options[kb_id]["rerank_provider_id"]
            if (
                vec_db
@@ -24,6 +24,7 @@ import asyncio
 import logging
 import os
 import sys
+import time
 from asyncio import Queue
 from collections import deque

@@ -148,7 +149,7 @@ class LogQueueHandler(logging.Handler):
        self.log_broker.publish(
            {
                "level": record.levelname,
-                "time": record.asctime,
+                "time": time.time(),
                "data": log_entry,
            },
        )
@@ -66,6 +66,9 @@ class ComponentType(str, Enum):
 class BaseMessageComponent(BaseModel):
    type: ComponentType

+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
    def toDict(self):
        data = {}
        for k, v in self.__dict__.items():
@@ -551,7 +554,7 @@ class Node(BaseMessageComponent):
    id: int | None = 0  # 忽略
    name: str | None = ""  # qq昵称
    uin: str | None = "0"  # qq号
-    content: list[BaseMessageComponent] | None = []
+    content: list[BaseMessageComponent] = []
    seq: str | list | None = ""  # 忽略
    time: int | None = 0  # 忽略

@@ -615,7 +618,7 @@ class Nodes(BaseMessageComponent):
            ret["messages"].append(d)
        return ret

-    async def to_dict(self):
+    async def to_dict(self) -> dict:
        """将 Nodes 转换为字典格式，适用于 OneBot JSON 格式"""
        ret = {"messages": []}
        for node in self.nodes:
@@ -714,15 +717,23 @@ class File(BaseMessageComponent):

        if self.url:
            await self._download_file()
-            return os.path.abspath(self.file_)
+            if self.file_:
+                return os.path.abspath(self.file_)

        return ""

    async def _download_file(self):
        """下载文件"""
+        if not self.url:
+            raise ValueError("Download failed: No URL provided in File component.")
        download_dir = os.path.join(get_astrbot_data_path(), "temp")
        os.makedirs(download_dir, exist_ok=True)
-        file_path = os.path.join(download_dir, f"{uuid.uuid4().hex}")
+        if self.name:
+            name, ext = os.path.splitext(self.name)
+            filename = f"{name}_{uuid.uuid4().hex[:8]}{ext}"
+        else:
+            filename = f"{uuid.uuid4().hex}"
+        file_path = os.path.join(download_dir, filename)
        await download_file(self.url, file_path)
        self.file_ = os.path.abspath(file_path)

@@ -98,8 +98,8 @@ class PersonaManager:
        self,
        persona_id: str,
        system_prompt: str,
-        begin_dialogs: list[str] = None,
-        tools: list[str] = None,
+        begin_dialogs: list[str] | None = None,
+        tools: list[str] | None = None,
    ) -> Persona:
        """创建新的 persona。tools 参数为 None 时表示使用所有工具，空列表表示不使用任何工具"""
        if await self.db.get_persona_by_id(persona_id):
@@ -24,7 +24,7 @@ class ContentSafetyCheckStage(Stage):
        self,
        event: AstrMessageEvent,
        check_text: str | None = None,
-    ) -> None | AsyncGenerator[None, None]:
+    ) -> AsyncGenerator[None, None]:
        """检查内容安全"""
        text = check_text if check_text else event.get_message_str()
        ok, info = self.strategy_selector.check(text)
@@ -3,7 +3,7 @@ from dataclasses import dataclass
 from astrbot.core.config import AstrBotConfig
 from astrbot.core.star import PluginManager

-from .context_utils import call_event_hook, call_handler, call_local_llm_tool
+from .context_utils import call_event_hook, call_handler


@dataclass
@@ -15,4 +15,3 @@ class PipelineContext:
    astrbot_config_id: str
    call_handler = call_handler
    call_event_hook = call_event_hook
-    call_local_llm_tool = call_local_llm_tool
@@ -3,8 +3,6 @@ import traceback
 import typing as T

 from astrbot import logger
-from astrbot.core.agent.run_context import ContextWrapper
-from astrbot.core.astr_agent_context import AstrAgentContext
 from astrbot.core.message.message_event_result import CommandResult, MessageEventResult
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.star import star_map
@@ -13,7 +11,7 @@ from astrbot.core.star.star_handler import EventType, star_handlers_registry

 async def call_handler(
    event: AstrMessageEvent,
-    handler: T.Callable[..., T.Awaitable[T.Any]],
+    handler: T.Callable[..., T.Awaitable[T.Any] | T.AsyncGenerator[T.Any, None]],
    *args,
    **kwargs,
 ) -> T.AsyncGenerator[T.Any, None]:
@@ -93,6 +91,7 @@ async def call_event_hook(
    )
    for handler in handlers:
        try:
+            assert inspect.iscoroutinefunction(handler.handler)
            logger.debug(
                f"hook({hook_type.name}) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}",
            )
@@ -107,66 +106,3 @@ async def call_event_hook(
            return True

    return event.is_stopped()
-
-
-async def call_local_llm_tool(
-    context: ContextWrapper[AstrAgentContext],
-    handler: T.Callable[..., T.Awaitable[T.Any]],
-    method_name: str,
-    *args,
-    **kwargs,
-) -> T.AsyncGenerator[T.Any, None]:
-    """执行本地 LLM 工具的处理函数并处理其返回结果"""
-    ready_to_call = None  # 一个协程或者异步生成器
-
-    trace_ = None
-
-    event = context.context.event
-
-    try:
-        if method_name == "run" or method_name == "decorator_handler":
-            ready_to_call = handler(event, *args, **kwargs)
-        elif method_name == "call":
-            ready_to_call = handler(context, *args, **kwargs)
-        else:
-            raise ValueError(f"未知的方法名: {method_name}")
-    except ValueError as e:
-        logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
-    except TypeError:
-        logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
-    except Exception as e:
-        trace_ = traceback.format_exc()
-        logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
-
-    if not ready_to_call:
-        return
-
-    if inspect.isasyncgen(ready_to_call):
-        _has_yielded = False
-        try:
-            async for ret in ready_to_call:
-                # 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
-                # 返回值只能是 MessageEventResult 或者 None（无返回值）
-                _has_yielded = True
-                if isinstance(ret, (MessageEventResult, CommandResult)):
-                    # 如果返回值是 MessageEventResult, 设置结果并继续
-                    event.set_result(ret)
-                    yield
-                else:
-                    # 如果返回值是 None, 则不设置结果并继续
-                    # 继续执行后续阶段
-                    yield ret
-            if not _has_yielded:
-                # 如果这个异步生成器没有执行到 yield 分支
-                yield
-        except Exception as e:
-            logger.error(f"Previous Error: {trace_}")
-            raise e
-    elif inspect.iscoroutine(ready_to_call):
-        # 如果只是一个协程, 直接执行
-        ret = await ready_to_call
-        if isinstance(ret, (MessageEventResult, CommandResult)):
-            event.set_result(ret)
-            yield
-        else:
-            yield ret
@@ -0,0 +1,48 @@
+from collections.abc import AsyncGenerator
+
+from astrbot.core import logger
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.star.session_llm_manager import SessionServiceManager
+
+from ...context import PipelineContext
+from ..stage import Stage
+from .agent_sub_stages.internal import InternalAgentSubStage
+from .agent_sub_stages.third_party import ThirdPartyAgentSubStage
+
+
+class AgentRequestSubStage(Stage):
+    async def initialize(self, ctx: PipelineContext) -> None:
+        self.ctx = ctx
+        self.config = ctx.astrbot_config
+
+        self.bot_wake_prefixs: list[str] = self.config["wake_prefix"]
+        self.prov_wake_prefix: str = self.config["provider_settings"]["wake_prefix"]
+        for bwp in self.bot_wake_prefixs:
+            if self.prov_wake_prefix.startswith(bwp):
+                logger.info(
+                    f"识别 LLM 聊天额外唤醒前缀 {self.prov_wake_prefix} 以机器人唤醒前缀 {bwp} 开头，已自动去除。",
+                )
+                self.prov_wake_prefix = self.prov_wake_prefix[len(bwp) :]
+
+        agent_runner_type = self.config["provider_settings"]["agent_runner_type"]
+        if agent_runner_type == "local":
+            self.agent_sub_stage = InternalAgentSubStage()
+        else:
+            self.agent_sub_stage = ThirdPartyAgentSubStage()
+        await self.agent_sub_stage.initialize(ctx)
+
+    async def process(self, event: AstrMessageEvent) -> AsyncGenerator[None, None]:
+        if not self.ctx.astrbot_config["provider_settings"]["enable"]:
+            logger.debug(
+                "This pipeline does not enable AI capability, skip processing."
+            )
+            return
+
+        if not SessionServiceManager.should_process_llm_request(event):
+            logger.debug(
+                f"The session {event.unified_msg_origin} has disabled AI capability, skipping processing."
+            )
+            return
+
+        async for resp in self.agent_sub_stage.process(event, self.prov_wake_prefix):
+            yield resp
@@ -0,0 +1,523 @@
+"""本地 Agent 模式的 LLM 调用 Stage"""
+
+import asyncio
+import copy
+import json
+from collections.abc import AsyncGenerator
+
+from astrbot.core import logger
+from astrbot.core.agent.tool import ToolSet
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.conversation_mgr import Conversation
+from astrbot.core.message.components import File, Image, Reply
+from astrbot.core.message.message_event_result import (
+    MessageChain,
+    MessageEventResult,
+    ResultContentType,
+)
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.provider import Provider
+from astrbot.core.provider.entities import (
+    LLMResponse,
+    ProviderRequest,
+)
+from astrbot.core.star.star_handler import EventType, star_map
+from astrbot.core.utils.file_extract import extract_file_moonshotai
+from astrbot.core.utils.metrics import Metric
+from astrbot.core.utils.session_lock import session_lock_manager
+
+from .....astr_agent_context import AgentContextWrapper
+from .....astr_agent_hooks import MAIN_AGENT_HOOKS
+from .....astr_agent_run_util import AgentRunner, run_agent
+from .....astr_agent_tool_exec import FunctionToolExecutor
+from ....context import PipelineContext, call_event_hook
+from ...stage import Stage
+from ...utils import KNOWLEDGE_BASE_QUERY_TOOL, retrieve_knowledge_base
+
+
+class InternalAgentSubStage(Stage):
+    async def initialize(self, ctx: PipelineContext) -> None:
+        self.ctx = ctx
+        conf = ctx.astrbot_config
+        settings = conf["provider_settings"]
+        self.max_context_length = settings["max_context_length"]  # int
+        self.dequeue_context_length: int = min(
+            max(1, settings["dequeue_context_length"]),
+            self.max_context_length - 1,
+        )
+        self.streaming_response: bool = settings["streaming_response"]
+        self.unsupported_streaming_strategy: str = settings[
+            "unsupported_streaming_strategy"
+        ]
+        self.max_step: int = settings.get("max_agent_step", 30)
+        self.tool_call_timeout: int = settings.get("tool_call_timeout", 60)
+        if isinstance(self.max_step, bool):  # workaround: #2622
+            self.max_step = 30
+        self.show_tool_use: bool = settings.get("show_tool_use_status", True)
+        self.show_reasoning = settings.get("display_reasoning_text", False)
+        self.kb_agentic_mode: bool = conf.get("kb_agentic_mode", False)
+
+        file_extract_conf: dict = settings.get("file_extract", {})
+        self.file_extract_enabled: bool = file_extract_conf.get("enable", False)
+        self.file_extract_prov: str = file_extract_conf.get("provider", "moonshotai")
+        self.file_extract_msh_api_key: str = file_extract_conf.get(
+            "moonshotai_api_key", ""
+        )
+
+        self.conv_manager = ctx.plugin_manager.context.conversation_manager
+
+    def _select_provider(self, event: AstrMessageEvent):
+        """选择使用的 LLM 提供商"""
+        sel_provider = event.get_extra("selected_provider")
+        _ctx = self.ctx.plugin_manager.context
+        if sel_provider and isinstance(sel_provider, str):
+            provider = _ctx.get_provider_by_id(sel_provider)
+            if not provider:
+                logger.error(f"未找到指定的提供商: {sel_provider}。")
+            return provider
+
+        return _ctx.get_using_provider(umo=event.unified_msg_origin)
+
+    async def _get_session_conv(self, event: AstrMessageEvent) -> Conversation:
+        umo = event.unified_msg_origin
+        conv_mgr = self.conv_manager
+
+        # 获取对话上下文
+        cid = await conv_mgr.get_curr_conversation_id(umo)
+        if not cid:
+            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
+        conversation = await conv_mgr.get_conversation(umo, cid)
+        if not conversation:
+            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
+            conversation = await conv_mgr.get_conversation(umo, cid)
+        if not conversation:
+            raise RuntimeError("无法创建新的对话。")
+        return conversation
+
+    async def _apply_kb(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+    ):
+        """Apply knowledge base context to the provider request"""
+        if not self.kb_agentic_mode:
+            if req.prompt is None:
+                return
+            try:
+                kb_result = await retrieve_knowledge_base(
+                    query=req.prompt,
+                    umo=event.unified_msg_origin,
+                    context=self.ctx.plugin_manager.context,
+                )
+                if not kb_result:
+                    return
+                if req.system_prompt is not None:
+                    req.system_prompt += (
+                        f"\n\n[Related Knowledge Base Results]:\n{kb_result}"
+                    )
+            except Exception as e:
+                logger.error(f"Error occurred while retrieving knowledge base: {e}")
+        else:
+            if req.func_tool is None:
+                req.func_tool = ToolSet()
+            req.func_tool.add_tool(KNOWLEDGE_BASE_QUERY_TOOL)
+
+    async def _apply_file_extract(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+    ):
+        """Apply file extract to the provider request"""
+        file_paths = []
+        file_names = []
+        for comp in event.message_obj.message:
+            if isinstance(comp, File):
+                file_paths.append(await comp.get_file())
+                file_names.append(comp.name)
+            elif isinstance(comp, Reply) and comp.chain:
+                for reply_comp in comp.chain:
+                    if isinstance(reply_comp, File):
+                        file_paths.append(await reply_comp.get_file())
+                        file_names.append(reply_comp.name)
+        if not file_paths:
+            return
+        if not req.prompt:
+            req.prompt = "总结一下文件里面讲了什么？"
+        if self.file_extract_prov == "moonshotai":
+            if not self.file_extract_msh_api_key:
+                logger.error("Moonshot AI API key for file extract is not set")
+                return
+            file_contents = await asyncio.gather(
+                *[
+                    extract_file_moonshotai(file_path, self.file_extract_msh_api_key)
+                    for file_path in file_paths
+                ]
+            )
+        else:
+            logger.error(f"Unsupported file extract provider: {self.file_extract_prov}")
+            return
+
+        # add file extract results to contexts
+        for file_content, file_name in zip(file_contents, file_names):
+            req.contexts.append(
+                {
+                    "role": "system",
+                    "content": f"File Extract Results of user uploaded files:\n{file_content}\nFile Name: {file_name or 'Unknown'}",
+                },
+            )
+
+    def _truncate_contexts(
+        self,
+        contexts: list[dict],
+    ) -> list[dict]:
+        """截断上下文列表，确保不超过最大长度"""
+        if self.max_context_length == -1:
+            return contexts
+
+        if len(contexts) // 2 <= self.max_context_length:
+            return contexts
+
+        truncated_contexts = contexts[
+            -(self.max_context_length - self.dequeue_context_length + 1) * 2 :
+        ]
+        # 找到第一个role 为 user 的索引，确保上下文格式正确
+        index = next(
+            (
+                i
+                for i, item in enumerate(truncated_contexts)
+                if item.get("role") == "user"
+            ),
+            None,
+        )
+        if index is not None and index > 0:
+            truncated_contexts = truncated_contexts[index:]
+
+        return truncated_contexts
+
+    def _modalities_fix(
+        self,
+        provider: Provider,
+        req: ProviderRequest,
+    ):
+        """检查提供商的模态能力，清理请求中的不支持内容"""
+        if req.image_urls:
+            provider_cfg = provider.provider_config.get("modalities", ["image"])
+            if "image" not in provider_cfg:
+                logger.debug(f"用户设置提供商 {provider} 不支持图像，清空图像列表。")
+                req.image_urls = []
+        if req.func_tool:
+            provider_cfg = provider.provider_config.get("modalities", ["tool_use"])
+            # 如果模型不支持工具使用，但请求中包含工具列表，则清空。
+            if "tool_use" not in provider_cfg:
+                logger.debug(
+                    f"用户设置提供商 {provider} 不支持工具使用，清空工具列表。",
+                )
+                req.func_tool = None
+
+    def _plugin_tool_fix(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+    ):
+        """根据事件中的插件设置，过滤请求中的工具列表"""
+        if event.plugins_name is not None and req.func_tool:
+            new_tool_set = ToolSet()
+            for tool in req.func_tool.tools:
+                mp = tool.handler_module_path
+                if not mp:
+                    continue
+                plugin = star_map.get(mp)
+                if not plugin:
+                    continue
+                if plugin.name in event.plugins_name or plugin.reserved:
+                    new_tool_set.add_tool(tool)
+            req.func_tool = new_tool_set
+
+    async def _handle_webchat(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+        prov: Provider,
+    ):
+        """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
+        if not req.conversation:
+            return
+        conversation = await self.conv_manager.get_conversation(
+            event.unified_msg_origin,
+            req.conversation.cid,
+        )
+        if conversation and not req.conversation.title:
+            messages = json.loads(conversation.history)
+            latest_pair = messages[-2:]
+            if not latest_pair:
+                return
+            content = latest_pair[0].get("content", "")
+            if isinstance(content, list):
+                # 多模态
+                text_parts = []
+                for item in content:
+                    if isinstance(item, dict):
+                        if item.get("type") == "text":
+                            text_parts.append(item.get("text", ""))
+                        elif item.get("type") == "image":
+                            text_parts.append("[图片]")
+                    elif isinstance(item, str):
+                        text_parts.append(item)
+                cleaned_text = "User: " + " ".join(text_parts).strip()
+            elif isinstance(content, str):
+                cleaned_text = "User: " + content.strip()
+            else:
+                return
+            logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
+            llm_resp = await prov.text_chat(
+                system_prompt="You are expert in summarizing user's query.",
+                prompt=(
+                    f"Please summarize the following query of user:\n"
+                    f"{cleaned_text}\n"
+                    "Only output the summary within 10 words, DO NOT INCLUDE any other text."
+                    "You must use the same language as the user."
+                    "If you think the dialog is too short to summarize, only output a special mark: `<None>`"
+                ),
+            )
+            if llm_resp and llm_resp.completion_text:
+                title = llm_resp.completion_text.strip()
+                if not title or "<None>" in title:
+                    return
+                await self.conv_manager.update_conversation_title(
+                    unified_msg_origin=event.unified_msg_origin,
+                    title=title,
+                    conversation_id=req.conversation.cid,
+                )
+
+    async def _save_to_history(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+        llm_response: LLMResponse | None,
+    ):
+        if (
+            not req
+            or not req.conversation
+            or not llm_response
+            or llm_response.role != "assistant"
+        ):
+            return
+
+        if not llm_response.completion_text and not req.tool_calls_result:
+            logger.debug("LLM 响应为空，不保存记录。")
+            return
+
+        if req.contexts is None:
+            req.contexts = []
+
+        # 历史上下文
+        messages = copy.deepcopy(req.contexts)
+        # 这一轮对话请求的用户输入
+        messages.append(await req.assemble_context())
+        # 这一轮对话的 LLM 响应
+        if req.tool_calls_result:
+            if not isinstance(req.tool_calls_result, list):
+                messages.extend(req.tool_calls_result.to_openai_messages())
+            elif isinstance(req.tool_calls_result, list):
+                for tcr in req.tool_calls_result:
+                    messages.extend(tcr.to_openai_messages())
+        messages.append({"role": "assistant", "content": llm_response.completion_text})
+        messages = list(filter(lambda item: "_no_save" not in item, messages))
+        await self.conv_manager.update_conversation(
+            event.unified_msg_origin,
+            req.conversation.cid,
+            history=messages,
+        )
+
+    def _fix_messages(self, messages: list[dict]) -> list[dict]:
+        """验证并且修复上下文"""
+        fixed_messages = []
+        for message in messages:
+            if message.get("role") == "tool":
+                # tool block 前面必须要有 user 和 assistant block
+                if len(fixed_messages) < 2:
+                    # 这种情况可能是上下文被截断导致的
+                    # 我们直接将之前的上下文都清空
+                    fixed_messages = []
+                else:
+                    fixed_messages.append(message)
+            else:
+                fixed_messages.append(message)
+        return fixed_messages
+
+    async def process(
+        self, event: AstrMessageEvent, provider_wake_prefix: str
+    ) -> AsyncGenerator[None, None]:
+        req: ProviderRequest | None = None
+
+        provider = self._select_provider(event)
+        if provider is None:
+            return
+        if not isinstance(provider, Provider):
+            logger.error(f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。")
+            return
+
+        streaming_response = self.streaming_response
+        if (enable_streaming := event.get_extra("enable_streaming")) is not None:
+            streaming_response = bool(enable_streaming)
+
+        logger.debug("ready to request llm provider")
+        async with session_lock_manager.acquire_lock(event.unified_msg_origin):
+            logger.debug("acquired session lock for llm request")
+            if event.get_extra("provider_request"):
+                req = event.get_extra("provider_request")
+                assert isinstance(req, ProviderRequest), (
+                    "provider_request 必须是 ProviderRequest 类型。"
+                )
+
+                if req.conversation:
+                    req.contexts = json.loads(req.conversation.history)
+
+            else:
+                req = ProviderRequest()
+                req.prompt = ""
+                req.image_urls = []
+                if sel_model := event.get_extra("selected_model"):
+                    req.model = sel_model
+                if provider_wake_prefix and not event.message_str.startswith(
+                    provider_wake_prefix
+                ):
+                    return
+
+                req.prompt = event.message_str[len(provider_wake_prefix) :]
+                # func_tool selection 现在已经转移到 packages/astrbot 插件中进行选择。
+                # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
+                for comp in event.message_obj.message:
+                    if isinstance(comp, Image):
+                        image_path = await comp.convert_to_file_path()
+                        req.image_urls.append(image_path)
+
+                conversation = await self._get_session_conv(event)
+                req.conversation = conversation
+                req.contexts = json.loads(conversation.history)
+
+                event.set_extra("provider_request", req)
+
+            # fix contexts json str
+            if isinstance(req.contexts, str):
+                req.contexts = json.loads(req.contexts)
+
+            # apply file extract
+            if self.file_extract_enabled:
+                try:
+                    await self._apply_file_extract(event, req)
+                except Exception as e:
+                    logger.error(f"Error occurred while applying file extract: {e}")
+
+            if not req.prompt and not req.image_urls:
+                return
+
+            # call event hook
+            if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
+                return
+
+            # apply knowledge base feature
+            await self._apply_kb(event, req)
+
+            # truncate contexts to fit max length
+            if req.contexts:
+                req.contexts = self._truncate_contexts(req.contexts)
+                self._fix_messages(req.contexts)
+
+            # session_id
+            if not req.session_id:
+                req.session_id = event.unified_msg_origin
+
+            # check provider modalities, if provider does not support image/tool_use, clear them in request.
+            self._modalities_fix(provider, req)
+
+            # filter tools, only keep tools from this pipeline's selected plugins
+            self._plugin_tool_fix(event, req)
+
+            stream_to_general = (
+                self.unsupported_streaming_strategy == "turn_off"
+                and not event.platform_meta.support_streaming_message
+            )
+            # 备份 req.contexts
+            backup_contexts = copy.deepcopy(req.contexts)
+
+            # run agent
+            agent_runner = AgentRunner()
+            logger.debug(
+                f"handle provider[id: {provider.provider_config['id']}] request: {req}",
+            )
+            astr_agent_ctx = AstrAgentContext(
+                context=self.ctx.plugin_manager.context,
+                event=event,
+            )
+            await agent_runner.reset(
+                provider=provider,
+                request=req,
+                run_context=AgentContextWrapper(
+                    context=astr_agent_ctx,
+                    tool_call_timeout=self.tool_call_timeout,
+                ),
+                tool_executor=FunctionToolExecutor(),
+                agent_hooks=MAIN_AGENT_HOOKS,
+                streaming=streaming_response,
+            )
+
+            if streaming_response and not stream_to_general:
+                # 流式响应
+                event.set_result(
+                    MessageEventResult()
+                    .set_result_content_type(ResultContentType.STREAMING_RESULT)
+                    .set_async_stream(
+                        run_agent(
+                            agent_runner,
+                            self.max_step,
+                            self.show_tool_use,
+                            show_reasoning=self.show_reasoning,
+                        ),
+                    ),
+                )
+                yield
+                if agent_runner.done():
+                    if final_llm_resp := agent_runner.get_final_llm_resp():
+                        if final_llm_resp.completion_text:
+                            chain = (
+                                MessageChain()
+                                .message(final_llm_resp.completion_text)
+                                .chain
+                            )
+                        elif final_llm_resp.result_chain:
+                            chain = final_llm_resp.result_chain.chain
+                        else:
+                            chain = MessageChain().chain
+                        event.set_result(
+                            MessageEventResult(
+                                chain=chain,
+                                result_content_type=ResultContentType.STREAMING_FINISH,
+                            ),
+                        )
+            else:
+                async for _ in run_agent(
+                    agent_runner,
+                    self.max_step,
+                    self.show_tool_use,
+                    stream_to_general,
+                    show_reasoning=self.show_reasoning,
+                ):
+                    yield
+
+            # 恢复备份的 contexts
+            req.contexts = backup_contexts
+
+            await self._save_to_history(event, req, agent_runner.get_final_llm_resp())
+
+        # 异步处理 WebChat 特殊情况
+        if event.get_platform_name() == "webchat":
+            asyncio.create_task(self._handle_webchat(event, req, provider))
+
+        asyncio.create_task(
+            Metric.upload(
+                llm_tick=1,
+                model_name=agent_runner.provider.get_model(),
+                provider_type=agent_runner.provider.meta().type,
+            ),
+        )
@@ -0,0 +1,205 @@
+import asyncio
+from collections.abc import AsyncGenerator
+from typing import TYPE_CHECKING
+
+from astrbot.core import astrbot_config, logger
+from astrbot.core.agent.runners.coze.coze_agent_runner import CozeAgentRunner
+from astrbot.core.agent.runners.dashscope.dashscope_agent_runner import (
+    DashscopeAgentRunner,
+)
+from astrbot.core.agent.runners.dify.dify_agent_runner import DifyAgentRunner
+from astrbot.core.message.components import Image
+from astrbot.core.message.message_event_result import (
+    MessageChain,
+    MessageEventResult,
+    ResultContentType,
+)
+
+if TYPE_CHECKING:
+    from astrbot.core.agent.runners.base import BaseAgentRunner
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.provider.entities import (
+    ProviderRequest,
+)
+from astrbot.core.star.star_handler import EventType
+from astrbot.core.utils.metrics import Metric
+
+from .....astr_agent_context import AgentContextWrapper, AstrAgentContext
+from .....astr_agent_hooks import MAIN_AGENT_HOOKS
+from ....context import PipelineContext, call_event_hook
+from ...stage import Stage
+
+AGENT_RUNNER_TYPE_KEY = {
+    "dify": "dify_agent_runner_provider_id",
+    "coze": "coze_agent_runner_provider_id",
+    "dashscope": "dashscope_agent_runner_provider_id",
+}
+
+
+async def run_third_party_agent(
+    runner: "BaseAgentRunner",
+    stream_to_general: bool = False,
+) -> AsyncGenerator[MessageChain | None, None]:
+    """
+    运行第三方 agent runner 并转换响应格式
+    类似于 run_agent 函数，但专门处理第三方 agent runner
+    """
+    try:
+        async for resp in runner.step_until_done(max_step=30):  # type: ignore[misc]
+            if resp.type == "streaming_delta":
+                if stream_to_general:
+                    continue
+                yield resp.data["chain"]
+            elif resp.type == "llm_result":
+                if stream_to_general:
+                    yield resp.data["chain"]
+    except Exception as e:
+        logger.error(f"Third party agent runner error: {e}")
+        err_msg = (
+            f"\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n"
+            f"错误信息: {e!s}\n\n请在平台日志查看和分享错误详情。\n"
+        )
+        yield MessageChain().message(err_msg)
+
+
+class ThirdPartyAgentSubStage(Stage):
+    async def initialize(self, ctx: PipelineContext) -> None:
+        self.ctx = ctx
+        self.conf = ctx.astrbot_config
+        self.runner_type = self.conf["provider_settings"]["agent_runner_type"]
+        self.prov_id = self.conf["provider_settings"].get(
+            AGENT_RUNNER_TYPE_KEY.get(self.runner_type, ""),
+            "",
+        )
+        settings = ctx.astrbot_config["provider_settings"]
+        self.streaming_response: bool = settings["streaming_response"]
+        self.unsupported_streaming_strategy: str = settings[
+            "unsupported_streaming_strategy"
+        ]
+
+    async def process(
+        self, event: AstrMessageEvent, provider_wake_prefix: str
+    ) -> AsyncGenerator[None, None]:
+        req: ProviderRequest | None = None
+
+        if provider_wake_prefix and not event.message_str.startswith(
+            provider_wake_prefix
+        ):
+            return
+
+        self.prov_cfg: dict = next(
+            (p for p in astrbot_config["provider"] if p["id"] == self.prov_id),
+            {},
+        )
+        if not self.prov_id:
+            logger.error("没有填写 Agent Runner 提供商 ID，请前往配置页面配置。")
+            return
+        if not self.prov_cfg:
+            logger.error(
+                f"Agent Runner 提供商 {self.prov_id} 配置不存在，请前往配置页面修改配置。"
+            )
+            return
+
+        # make provider request
+        req = ProviderRequest()
+        req.session_id = event.unified_msg_origin
+        req.prompt = event.message_str[len(provider_wake_prefix) :]
+        for comp in event.message_obj.message:
+            if isinstance(comp, Image):
+                image_path = await comp.convert_to_base64()
+                req.image_urls.append(image_path)
+
+        if not req.prompt and not req.image_urls:
+            return
+
+        # call event hook
+        if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
+            return
+
+        if self.runner_type == "dify":
+            runner = DifyAgentRunner[AstrAgentContext]()
+        elif self.runner_type == "coze":
+            runner = CozeAgentRunner[AstrAgentContext]()
+        elif self.runner_type == "dashscope":
+            runner = DashscopeAgentRunner[AstrAgentContext]()
+        else:
+            raise ValueError(
+                f"Unsupported third party agent runner type: {self.runner_type}",
+            )
+
+        astr_agent_ctx = AstrAgentContext(
+            context=self.ctx.plugin_manager.context,
+            event=event,
+        )
+
+        streaming_response = self.streaming_response
+        if (enable_streaming := event.get_extra("enable_streaming")) is not None:
+            streaming_response = bool(enable_streaming)
+
+        stream_to_general = (
+            self.unsupported_streaming_strategy == "turn_off"
+            and not event.platform_meta.support_streaming_message
+        )
+
+        await runner.reset(
+            request=req,
+            run_context=AgentContextWrapper(
+                context=astr_agent_ctx,
+                tool_call_timeout=60,
+            ),
+            agent_hooks=MAIN_AGENT_HOOKS,
+            provider_config=self.prov_cfg,
+            streaming=streaming_response,
+        )
+
+        if streaming_response and not stream_to_general:
+            # 流式响应
+            event.set_result(
+                MessageEventResult()
+                .set_result_content_type(ResultContentType.STREAMING_RESULT)
+                .set_async_stream(
+                    run_third_party_agent(
+                        runner,
+                        stream_to_general=False,
+                    ),
+                ),
+            )
+            yield
+            if runner.done():
+                final_resp = runner.get_final_llm_resp()
+                if final_resp and final_resp.result_chain:
+                    event.set_result(
+                        MessageEventResult(
+                            chain=final_resp.result_chain.chain or [],
+                            result_content_type=ResultContentType.STREAMING_FINISH,
+                        ),
+                    )
+        else:
+            # 非流式响应或转换为普通响应
+            async for _ in run_third_party_agent(
+                runner,
+                stream_to_general=stream_to_general,
+            ):
+                yield
+
+            final_resp = runner.get_final_llm_resp()
+
+            if not final_resp or not final_resp.result_chain:
+                logger.warning("Agent Runner 未返回最终结果。")
+                return
+
+            event.set_result(
+                MessageEventResult(
+                    chain=final_resp.result_chain.chain or [],
+                    result_content_type=ResultContentType.LLM_RESULT,
+                ),
+            )
+            yield
+
+        asyncio.create_task(
+            Metric.upload(
+                llm_tick=1,
+                model_name=self.runner_type,
+                provider_type=self.runner_type,
+            ),
+        )
@@ -1,723 +0,0 @@
-"""本地 Agent 模式的 LLM 调用 Stage"""
-
-import asyncio
-import copy
-import json
-import traceback
-from collections.abc import AsyncGenerator
-from typing import Any
-
-from mcp.types import CallToolResult
-
-from astrbot.core import logger
-from astrbot.core.agent.handoff import HandoffTool
-from astrbot.core.agent.hooks import BaseAgentRunHooks
-from astrbot.core.agent.mcp_client import MCPTool
-from astrbot.core.agent.run_context import ContextWrapper
-from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
-from astrbot.core.agent.tool import FunctionTool, ToolSet
-from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
-from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.conversation_mgr import Conversation
-from astrbot.core.message.components import Image
-from astrbot.core.message.message_event_result import (
-    MessageChain,
-    MessageEventResult,
-    ResultContentType,
-)
-from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.provider import Provider
-from astrbot.core.provider.entities import (
-    LLMResponse,
-    ProviderRequest,
-)
-from astrbot.core.provider.register import llm_tools
-from astrbot.core.star.session_llm_manager import SessionServiceManager
-from astrbot.core.star.star_handler import EventType, star_map
-from astrbot.core.utils.metrics import Metric
-
-from ...context import PipelineContext, call_event_hook, call_local_llm_tool
-from ..stage import Stage
-from ..utils import inject_kb_context
-
-try:
-    import mcp
-except (ModuleNotFoundError, ImportError):
-    logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
-
-
-AgentContextWrapper = ContextWrapper[AstrAgentContext]
-AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
-
-
-class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
-    @classmethod
-    async def execute(cls, tool, run_context, **tool_args):
-        """执行函数调用。
-
-        Args:
-            event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
-            **kwargs: 函数调用的参数。
-
-        Returns:
-            AsyncGenerator[None | mcp.types.CallToolResult, None]
-
-        """
-        if isinstance(tool, HandoffTool):
-            async for r in cls._execute_handoff(tool, run_context, **tool_args):
-                yield r
-            return
-
-        elif isinstance(tool, MCPTool):
-            async for r in cls._execute_mcp(tool, run_context, **tool_args):
-                yield r
-            return
-
-        else:
-            async for r in cls._execute_local(tool, run_context, **tool_args):
-                yield r
-            return
-
-    @classmethod
-    async def _execute_handoff(
-        cls,
-        tool: HandoffTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        input_ = tool_args.get("input", "agent")
-        agent_runner = AgentRunner()
-
-        # make toolset for the agent
-        tools = tool.agent.tools
-        if tools:
-            toolset = ToolSet()
-            for t in tools:
-                if isinstance(t, str):
-                    _t = llm_tools.get_func(t)
-                    if _t:
-                        toolset.add_tool(_t)
-                elif isinstance(t, FunctionTool):
-                    toolset.add_tool(t)
-        else:
-            toolset = None
-
-        request = ProviderRequest(
-            prompt=input_,
-            system_prompt=tool.description or "",
-            image_urls=[],  # 暂时不传递原始 agent 的上下文
-            contexts=[],  # 暂时不传递原始 agent 的上下文
-            func_tool=toolset,
-        )
-        astr_agent_ctx = AstrAgentContext(
-            provider=run_context.context.provider,
-            first_provider_request=run_context.context.first_provider_request,
-            curr_provider_request=request,
-            streaming=run_context.context.streaming,
-            event=run_context.context.event,
-        )
-
-        event = run_context.context.event
-
-        logger.debug(f"正在将任务委托给 Agent: {tool.agent.name}, input: {input_}")
-        await event.send(
-            MessageChain().message("✨ 正在将任务委托给 Agent: " + tool.agent.name),
-        )
-
-        await agent_runner.reset(
-            provider=run_context.context.provider,
-            request=request,
-            run_context=AgentContextWrapper(
-                context=astr_agent_ctx,
-                tool_call_timeout=run_context.tool_call_timeout,
-            ),
-            tool_executor=FunctionToolExecutor(),
-            agent_hooks=tool.agent.run_hooks or BaseAgentRunHooks[AstrAgentContext](),
-            streaming=run_context.context.streaming,
-        )
-
-        async for _ in run_agent(agent_runner, 15, True):
-            pass
-
-        if agent_runner.done():
-            llm_response = agent_runner.get_final_llm_resp()
-
-            if not llm_response:
-                text_content = mcp.types.TextContent(
-                    type="text",
-                    text=f"error when deligate task to {tool.agent.name}",
-                )
-                yield mcp.types.CallToolResult(content=[text_content])
-                return
-
-            logger.debug(
-                f"Agent  {tool.agent.name} 任务完成, response: {llm_response.completion_text}",
-            )
-
-            result = (
-                f"Agent {tool.agent.name} respond with: {llm_response.completion_text}\n\n"
-                "Note: If the result is error or need user provide more information, please provide more information to the agent(you can ask user for more information first)."
-            )
-
-            text_content = mcp.types.TextContent(
-                type="text",
-                text=result,
-            )
-            yield mcp.types.CallToolResult(content=[text_content])
-        else:
-            text_content = mcp.types.TextContent(
-                type="text",
-                text=f"error when deligate task to {tool.agent.name}",
-            )
-            yield mcp.types.CallToolResult(content=[text_content])
-        return
-
-    @classmethod
-    async def _execute_local(
-        cls,
-        tool: FunctionTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        event = run_context.context.event
-        if not event:
-            raise ValueError("Event must be provided for local function tools.")
-
-        is_override_call = False
-        for ty in type(tool).mro():
-            if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
-                logger.debug(f"Found call in: {ty}")
-                is_override_call = True
-                break
-
-        # 检查 tool 下有没有 run 方法
-        if not tool.handler and not hasattr(tool, "run") and not is_override_call:
-            raise ValueError("Tool must have a valid handler or override 'run' method.")
-
-        awaitable = None
-        method_name = ""
-        if tool.handler:
-            awaitable = tool.handler
-            method_name = "decorator_handler"
-        elif is_override_call:
-            awaitable = tool.call
-            method_name = "call"
-        elif hasattr(tool, "run"):
-            awaitable = getattr(tool, "run")
-            method_name = "run"
-        if awaitable is None:
-            raise ValueError("Tool must have a valid handler or override 'run' method.")
-
-        wrapper = call_local_llm_tool(
-            context=run_context,
-            handler=awaitable,
-            method_name=method_name,
-            **tool_args,
-        )
-        while True:
-            try:
-                resp = await asyncio.wait_for(
-                    anext(wrapper),
-                    timeout=run_context.tool_call_timeout,
-                )
-                if resp is not None:
-                    if isinstance(resp, mcp.types.CallToolResult):
-                        yield resp
-                    else:
-                        text_content = mcp.types.TextContent(
-                            type="text",
-                            text=str(resp),
-                        )
-                        yield mcp.types.CallToolResult(content=[text_content])
-                else:
-                    # NOTE: Tool 在这里直接请求发送消息给用户
-                    # TODO: 是否需要判断 event.get_result() 是否为空?
-                    # 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
-                    if res := run_context.context.event.get_result():
-                        if res.chain:
-                            try:
-                                await event.send(
-                                    MessageChain(
-                                        chain=res.chain,
-                                        type="tool_direct_result",
-                                    )
-                                )
-                            except Exception as e:
-                                logger.error(
-                                    f"Tool 直接发送消息失败: {e}",
-                                    exc_info=True,
-                                )
-                    yield None
-            except asyncio.TimeoutError:
-                raise Exception(
-                    f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
-                )
-            except StopAsyncIteration:
-                break
-
-    @classmethod
-    async def _execute_mcp(
-        cls,
-        tool: FunctionTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        res = await tool.call(run_context, **tool_args)
-        if not res:
-            return
-        yield res
-
-
-class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
-    async def on_agent_done(self, run_context, llm_response):
-        # 执行事件钩子
-        await call_event_hook(
-            run_context.context.event,
-            EventType.OnLLMResponseEvent,
-            llm_response,
-        )
-
-    async def on_tool_end(
-        self,
-        run_context: ContextWrapper[AstrAgentContext],
-        tool: FunctionTool[Any],
-        tool_args: dict | None,
-        tool_result: CallToolResult | None,
-    ):
-        run_context.context.event.clear_result()
-
-
-MAIN_AGENT_HOOKS = MainAgentHooks()
-
-
-async def run_agent(
-    agent_runner: AgentRunner,
-    max_step: int = 30,
-    show_tool_use: bool = True,
-) -> AsyncGenerator[MessageChain, None]:
-    step_idx = 0
-    astr_event = agent_runner.run_context.context.event
-    while step_idx < max_step:
-        step_idx += 1
-        try:
-            async for resp in agent_runner.step():
-                if astr_event.is_stopped():
-                    return
-                if resp.type == "tool_call_result":
-                    msg_chain = resp.data["chain"]
-                    if msg_chain.type == "tool_direct_result":
-                        # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
-                        resp.data["chain"].type = "tool_call_result"
-                        await astr_event.send(resp.data["chain"])
-                        continue
-                    # 对于其他情况，暂时先不处理
-                    continue
-                elif resp.type == "tool_call":
-                    if agent_runner.streaming:
-                        # 用来标记流式响应需要分节
-                        yield MessageChain(chain=[], type="break")
-                    if show_tool_use or astr_event.get_platform_name() == "webchat":
-                        resp.data["chain"].type = "tool_call"
-                        await astr_event.send(resp.data["chain"])
-                    continue
-
-                if not agent_runner.streaming:
-                    content_typ = (
-                        ResultContentType.LLM_RESULT
-                        if resp.type == "llm_result"
-                        else ResultContentType.GENERAL_RESULT
-                    )
-                    astr_event.set_result(
-                        MessageEventResult(
-                            chain=resp.data["chain"].chain,
-                            result_content_type=content_typ,
-                        ),
-                    )
-                    yield
-                    astr_event.clear_result()
-                elif resp.type == "streaming_delta":
-                    yield resp.data["chain"]  # MessageChain
-            if agent_runner.done():
-                break
-
-        except Exception as e:
-            logger.error(traceback.format_exc())
-            err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
-            if agent_runner.streaming:
-                yield MessageChain().message(err_msg)
-            else:
-                astr_event.set_result(MessageEventResult().message(err_msg))
-            return
-
-
-class LLMRequestSubStage(Stage):
-    async def initialize(self, ctx: PipelineContext) -> None:
-        self.ctx = ctx
-        conf = ctx.astrbot_config
-        settings = conf["provider_settings"]
-        self.bot_wake_prefixs: list[str] = conf["wake_prefix"]  # list
-        self.provider_wake_prefix: str = settings["wake_prefix"]  # str
-        self.max_context_length = settings["max_context_length"]  # int
-        self.dequeue_context_length: int = min(
-            max(1, settings["dequeue_context_length"]),
-            self.max_context_length - 1,
-        )
-        self.streaming_response: bool = settings["streaming_response"]
-        self.max_step: int = settings.get("max_agent_step", 30)
-        self.tool_call_timeout: int = settings.get("tool_call_timeout", 60)
-        if isinstance(self.max_step, bool):  # workaround: #2622
-            self.max_step = 30
-        self.show_tool_use: bool = settings.get("show_tool_use_status", True)
-
-        for bwp in self.bot_wake_prefixs:
-            if self.provider_wake_prefix.startswith(bwp):
-                logger.info(
-                    f"识别 LLM 聊天额外唤醒前缀 {self.provider_wake_prefix} 以机器人唤醒前缀 {bwp} 开头，已自动去除。",
-                )
-                self.provider_wake_prefix = self.provider_wake_prefix[len(bwp) :]
-
-        self.conv_manager = ctx.plugin_manager.context.conversation_manager
-
-    def _select_provider(self, event: AstrMessageEvent):
-        """选择使用的 LLM 提供商"""
-        sel_provider = event.get_extra("selected_provider")
-        _ctx = self.ctx.plugin_manager.context
-        if sel_provider and isinstance(sel_provider, str):
-            provider = _ctx.get_provider_by_id(sel_provider)
-            if not provider:
-                logger.error(f"未找到指定的提供商: {sel_provider}。")
-            return provider
-
-        return _ctx.get_using_provider(umo=event.unified_msg_origin)
-
-    async def _get_session_conv(self, event: AstrMessageEvent) -> Conversation:
-        umo = event.unified_msg_origin
-        conv_mgr = self.conv_manager
-
-        # 获取对话上下文
-        cid = await conv_mgr.get_curr_conversation_id(umo)
-        if not cid:
-            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
-        conversation = await conv_mgr.get_conversation(umo, cid)
-        if not conversation:
-            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
-            conversation = await conv_mgr.get_conversation(umo, cid)
-        if not conversation:
-            raise RuntimeError("无法创建新的对话。")
-        return conversation
-
-    async def process(
-        self,
-        event: AstrMessageEvent,
-        _nested: bool = False,
-    ) -> None | AsyncGenerator[None, None]:
-        req: ProviderRequest | None = None
-
-        if not self.ctx.astrbot_config["provider_settings"]["enable"]:
-            logger.debug("未启用 LLM 能力，跳过处理。")
-            return
-
-        # 检查会话级别的LLM启停状态
-        if not SessionServiceManager.should_process_llm_request(event):
-            logger.debug(f"会话 {event.unified_msg_origin} 禁用了 LLM，跳过处理。")
-            return
-
-        provider = self._select_provider(event)
-        if provider is None:
-            return
-        if not isinstance(provider, Provider):
-            logger.error(f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。")
-            return
-
-        if event.get_extra("provider_request"):
-            req = event.get_extra("provider_request")
-            assert isinstance(req, ProviderRequest), (
-                "provider_request 必须是 ProviderRequest 类型。"
-            )
-
-            if req.conversation:
-                req.contexts = json.loads(req.conversation.history)
-
-        else:
-            req = ProviderRequest(prompt="", image_urls=[])
-            if sel_model := event.get_extra("selected_model"):
-                req.model = sel_model
-            if self.provider_wake_prefix:
-                if not event.message_str.startswith(self.provider_wake_prefix):
-                    return
-            req.prompt = event.message_str[len(self.provider_wake_prefix) :]
-            # func_tool selection 现在已经转移到 packages/astrbot 插件中进行选择。
-            # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
-            for comp in event.message_obj.message:
-                if isinstance(comp, Image):
-                    image_path = await comp.convert_to_file_path()
-                    req.image_urls.append(image_path)
-
-            conversation = await self._get_session_conv(event)
-            req.conversation = conversation
-            req.contexts = json.loads(conversation.history)
-
-            event.set_extra("provider_request", req)
-
-        if not req.prompt and not req.image_urls:
-            return
-
-        # 应用知识库
-        try:
-            await inject_kb_context(
-                umo=event.unified_msg_origin,
-                p_ctx=self.ctx,
-                req=req,
-            )
-        except Exception as e:
-            logger.error(f"调用知识库时遇到问题: {e}")
-
-        # 执行请求 LLM 前事件钩子。
-        if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
-            return
-
-        if isinstance(req.contexts, str):
-            req.contexts = json.loads(req.contexts)
-
-        # max context length
-        if (
-            self.max_context_length != -1  # -1 为不限制
-            and len(req.contexts) // 2 > self.max_context_length
-        ):
-            logger.debug("上下文长度超过限制，将截断。")
-            req.contexts = req.contexts[
-                -(self.max_context_length - self.dequeue_context_length + 1) * 2 :
-            ]
-            # 找到第一个role 为 user 的索引，确保上下文格式正确
-            index = next(
-                (
-                    i
-                    for i, item in enumerate(req.contexts)
-                    if item.get("role") == "user"
-                ),
-                None,
-            )
-            if index is not None and index > 0:
-                req.contexts = req.contexts[index:]
-
-        # session_id
-        if not req.session_id:
-            req.session_id = event.unified_msg_origin
-
-        # fix messages
-        req.contexts = self.fix_messages(req.contexts)
-
-        # check provider modalities
-        # 如果提供商不支持图像/工具使用，但请求中包含图像/工具列表，则清空。图片转述等的检测和调用发生在这之前，因此这里可以这样处理。
-        if req.image_urls:
-            provider_cfg = provider.provider_config.get("modalities", ["image"])
-            if "image" not in provider_cfg:
-                logger.debug(f"用户设置提供商 {provider} 不支持图像，清空图像列表。")
-                req.image_urls = []
-        if req.func_tool:
-            provider_cfg = provider.provider_config.get("modalities", ["tool_use"])
-            # 如果模型不支持工具使用，但请求中包含工具列表，则清空。
-            if "tool_use" not in provider_cfg:
-                logger.debug(
-                    f"用户设置提供商 {provider} 不支持工具使用，清空工具列表。",
-                )
-                req.func_tool = None
-        # 插件可用性设置
-        if event.plugins_name is not None and req.func_tool:
-            new_tool_set = ToolSet()
-            for tool in req.func_tool.tools:
-                mp = tool.handler_module_path
-                if not mp:
-                    continue
-                plugin = star_map.get(mp)
-                if not plugin:
-                    continue
-                if plugin.name in event.plugins_name or plugin.reserved:
-                    new_tool_set.add_tool(tool)
-            req.func_tool = new_tool_set
-
-        # 备份 req.contexts
-        backup_contexts = copy.deepcopy(req.contexts)
-
-        # run agent
-        agent_runner = AgentRunner()
-        logger.debug(
-            f"handle provider[id: {provider.provider_config['id']}] request: {req}",
-        )
-        astr_agent_ctx = AstrAgentContext(
-            provider=provider,
-            first_provider_request=req,
-            curr_provider_request=req,
-            streaming=self.streaming_response,
-            event=event,
-        )
-        await agent_runner.reset(
-            provider=provider,
-            request=req,
-            run_context=AgentContextWrapper(
-                context=astr_agent_ctx,
-                tool_call_timeout=self.tool_call_timeout,
-            ),
-            tool_executor=FunctionToolExecutor(),
-            agent_hooks=MAIN_AGENT_HOOKS,
-            streaming=self.streaming_response,
-        )
-
-        if self.streaming_response:
-            # 流式响应
-            event.set_result(
-                MessageEventResult()
-                .set_result_content_type(ResultContentType.STREAMING_RESULT)
-                .set_async_stream(
-                    run_agent(agent_runner, self.max_step, self.show_tool_use),
-                ),
-            )
-            yield
-            if agent_runner.done():
-                if final_llm_resp := agent_runner.get_final_llm_resp():
-                    if final_llm_resp.completion_text:
-                        chain = (
-                            MessageChain().message(final_llm_resp.completion_text).chain
-                        )
-                    elif final_llm_resp.result_chain:
-                        chain = final_llm_resp.result_chain.chain
-                    else:
-                        chain = MessageChain().chain
-                    event.set_result(
-                        MessageEventResult(
-                            chain=chain,
-                            result_content_type=ResultContentType.STREAMING_FINISH,
-                        ),
-                    )
-        else:
-            async for _ in run_agent(agent_runner, self.max_step, self.show_tool_use):
-                yield
-
-        # 恢复备份的 contexts
-        req.contexts = backup_contexts
-
-        await self._save_to_history(event, req, agent_runner.get_final_llm_resp())
-
-        # 异步处理 WebChat 特殊情况
-        if event.get_platform_name() == "webchat":
-            asyncio.create_task(self._handle_webchat(event, req, provider))
-
-        asyncio.create_task(
-            Metric.upload(
-                llm_tick=1,
-                model_name=agent_runner.provider.get_model(),
-                provider_type=agent_runner.provider.meta().type,
-            ),
-        )
-
-    async def _handle_webchat(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        prov: Provider,
-    ):
-        """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
-        if not req.conversation:
-            return
-        conversation = await self.conv_manager.get_conversation(
-            event.unified_msg_origin,
-            req.conversation.cid,
-        )
-        if conversation and not req.conversation.title:
-            messages = json.loads(conversation.history)
-            latest_pair = messages[-2:]
-            if not latest_pair:
-                return
-            content = latest_pair[0].get("content", "")
-            if isinstance(content, list):
-                # 多模态
-                text_parts = []
-                for item in content:
-                    if isinstance(item, dict):
-                        if item.get("type") == "text":
-                            text_parts.append(item.get("text", ""))
-                        elif item.get("type") == "image":
-                            text_parts.append("[图片]")
-                    elif isinstance(item, str):
-                        text_parts.append(item)
-                cleaned_text = "User: " + " ".join(text_parts).strip()
-            elif isinstance(content, str):
-                cleaned_text = "User: " + content.strip()
-            else:
-                return
-            logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
-            llm_resp = await prov.text_chat(
-                system_prompt="You are expert in summarizing user's query.",
-                prompt=(
-                    f"Please summarize the following query of user:\n"
-                    f"{cleaned_text}\n"
-                    "Only output the summary within 10 words, DO NOT INCLUDE any other text."
-                    "You must use the same language as the user."
-                    "If you think the dialog is too short to summarize, only output a special mark: `<None>`"
-                ),
-            )
-            if llm_resp and llm_resp.completion_text:
-                logger.debug(
-                    f"WebChat 对话标题生成响应: {llm_resp.completion_text.strip()}",
-                )
-                title = llm_resp.completion_text.strip()
-                if not title or "<None>" in title:
-                    return
-                await self.conv_manager.update_conversation_title(
-                    unified_msg_origin=event.unified_msg_origin,
-                    title=title,
-                    conversation_id=req.conversation.cid,
-                )
-
-    async def _save_to_history(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        llm_response: LLMResponse | None,
-    ):
-        if (
-            not req
-            or not req.conversation
-            or not llm_response
-            or llm_response.role != "assistant"
-        ):
-            return
-
-        if not llm_response.completion_text and not req.tool_calls_result:
-            logger.debug("LLM 响应为空，不保存记录。")
-            return
-
-        # 历史上下文
-        messages = copy.deepcopy(req.contexts)
-        # 这一轮对话请求的用户输入
-        messages.append(await req.assemble_context())
-        # 这一轮对话的 LLM 响应
-        if req.tool_calls_result:
-            if not isinstance(req.tool_calls_result, list):
-                messages.extend(req.tool_calls_result.to_openai_messages())
-            elif isinstance(req.tool_calls_result, list):
-                for tcr in req.tool_calls_result:
-                    messages.extend(tcr.to_openai_messages())
-        messages.append({"role": "assistant", "content": llm_response.completion_text})
-        messages = list(filter(lambda item: "_no_save" not in item, messages))
-        await self.conv_manager.update_conversation(
-            event.unified_msg_origin,
-            req.conversation.cid,
-            history=messages,
-        )
-
-    def fix_messages(self, messages: list[dict]) -> list[dict]:
-        """验证并且修复上下文"""
-        fixed_messages = []
-        for message in messages:
-            if message.get("role") == "tool":
-                # tool block 前面必须要有 user 和 assistant block
-                if len(fixed_messages) < 2:
-                    # 这种情况可能是上下文被截断导致的
-                    # 我们直接将之前的上下文都清空
-                    fixed_messages = []
-                else:
-                    fixed_messages.append(message)
-            else:
-                fixed_messages.append(message)
-        return fixed_messages
@@ -16,7 +16,6 @@ from ..stage import Stage

 class StarRequestSubStage(Stage):
    async def initialize(self, ctx: PipelineContext) -> None:
-        self.curr_provider = ctx.plugin_manager.context.get_using_provider()
        self.prompt_prefix = ctx.astrbot_config["provider_settings"]["prompt_prefix"]
        self.identifier = ctx.astrbot_config["provider_settings"]["identifier"]
        self.ctx = ctx
@@ -24,7 +23,7 @@ class StarRequestSubStage(Stage):
    async def process(
        self,
        event: AstrMessageEvent,
-    ) -> None | AsyncGenerator[None, None]:
+    ) -> AsyncGenerator[Any, None]:
        activated_handlers: list[StarHandlerMetadata] = event.get_extra(
            "activated_handlers",
        )
@@ -1,13 +1,12 @@
 from collections.abc import AsyncGenerator

-from astrbot.core import logger
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.provider.entities import ProviderRequest
 from astrbot.core.star.star_handler import StarHandlerMetadata

 from ..context import PipelineContext
 from ..stage import Stage, register_stage
-from .method.llm_request import LLMRequestSubStage
+from .method.agent_request import AgentRequestSubStage
 from .method.star_request import StarRequestSubStage


@@ -17,9 +16,12 @@ class ProcessStage(Stage):
        self.ctx = ctx
        self.config = ctx.astrbot_config
        self.plugin_manager = ctx.plugin_manager
-        self.llm_request_sub_stage = LLMRequestSubStage()
-        await self.llm_request_sub_stage.initialize(ctx)

+        # initialize agent sub stage
+        self.agent_sub_stage = AgentRequestSubStage()
+        await self.agent_sub_stage.initialize(ctx)
+
+        # initialize star request sub stage
        self.star_request_sub_stage = StarRequestSubStage()
        await self.star_request_sub_stage.initialize(ctx)

@@ -39,7 +41,7 @@ class ProcessStage(Stage):
                    # Handler 的 LLM 请求
                    event.set_extra("provider_request", resp)
                    _t = False
-                    async for _ in self.llm_request_sub_stage.process(event):
+                    async for _ in self.agent_sub_stage.process(event):
                        _t = True
                        yield
                    if not _t:
@@ -58,14 +60,7 @@ class ProcessStage(Stage):
        ):
            # 是否有过发送操作 and 是否是被 @ 或者通过唤醒前缀
            if (
-                event.get_result() and not event.get_result().is_stopped()
+                event.get_result() and not event.is_stopped()
            ) or not event.get_result():
-                # 事件没有终止传播
-                provider = self.ctx.plugin_manager.context.get_using_provider()
-
-                if not provider:
-                    logger.info("未找到可用的 LLM 提供商，请先前往配置服务提供商。")
-                    return
-
-                async for _ in self.llm_request_sub_stage.process(event):
+                async for _ in self.agent_sub_stage.process(event):
                    yield
@@ -1,23 +1,64 @@
+from pydantic import Field
+from pydantic.dataclasses import dataclass
+
 from astrbot.api import logger, sp
-from astrbot.core.provider.entities import ProviderRequest
-
-from ..context import PipelineContext
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.agent.tool import FunctionTool, ToolExecResult
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.star.context import Context


-async def inject_kb_context(
+@dataclass
+class KnowledgeBaseQueryTool(FunctionTool[AstrAgentContext]):
+    name: str = "astr_kb_search"
+    description: str = (
+        "Query the knowledge base for facts or relevant context. "
+        "Use this tool when the user's question requires factual information, "
+        "definitions, background knowledge, or previously indexed content. "
+        "Only send short keywords or a concise question as the query."
+    )
+    parameters: dict = Field(
+        default_factory=lambda: {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "A concise keyword query for the knowledge base.",
+                },
+            },
+            "required": ["query"],
+        }
+    )
+
+    async def call(
+        self, context: ContextWrapper[AstrAgentContext], **kwargs
+    ) -> ToolExecResult:
+        query = kwargs.get("query", "")
+        if not query:
+            return "error: Query parameter is empty."
+        result = await retrieve_knowledge_base(
+            query=kwargs.get("query", ""),
+            umo=context.context.event.unified_msg_origin,
+            context=context.context.context,
+        )
+        if not result:
+            return "No relevant knowledge found."
+        return result
+
+
+async def retrieve_knowledge_base(
+    query: str,
    umo: str,
-    p_ctx: PipelineContext,
-    req: ProviderRequest,
-) -> None:
+    context: Context,
+) -> str | None:
    """Inject knowledge base context into the provider request

    Args:
        umo: Unique message object (session ID)
        p_ctx: Pipeline context
-        req: Provider request
-
    """
-    kb_mgr = p_ctx.plugin_manager.context.kb_manager
+    kb_mgr = context.kb_manager
+    config = context.get_config(umo=umo)

    # 1. 优先读取会话级配置
    session_config = await sp.session_get(umo, "kb_config", default={})
@@ -54,18 +95,18 @@ async def inject_kb_context(

        logger.debug(f"[知识库] 使用会话级配置，知识库数量: {len(kb_names)}")
    else:
-        kb_names = p_ctx.astrbot_config.get("kb_names", [])
-        top_k = p_ctx.astrbot_config.get("kb_final_top_k", 5)
+        kb_names = config.get("kb_names", [])
+        top_k = config.get("kb_final_top_k", 5)
        logger.debug(f"[知识库] 使用全局配置，知识库数量: {len(kb_names)}")

-    top_k_fusion = p_ctx.astrbot_config.get("kb_fusion_top_k", 20)
+    top_k_fusion = config.get("kb_fusion_top_k", 20)

    if not kb_names:
        return

    logger.debug(f"[知识库] 开始检索知识库，数量: {len(kb_names)}, top_k={top_k}")
    kb_context = await kb_mgr.retrieve(
-        query=req.prompt,
+        query=query,
        kb_names=kb_names,
        top_k_fusion=top_k_fusion,
        top_m_final=top_k,
@@ -78,4 +119,7 @@ async def inject_kb_context(
    if formatted:
        results = kb_context.get("results", [])
        logger.debug(f"[知识库] 为会话 {umo} 注入了 {len(results)} 条相关知识块")
-        req.system_prompt = f"{formatted}\n\n{req.system_prompt or ''}"
+        return formatted
+
+
+KNOWLEDGE_BASE_QUERY_TOOL = KnowledgeBaseQueryTool()
@@ -10,7 +10,6 @@ from astrbot.core.message.message_event_result import MessageChain, ResultConten
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.star_handler import EventType
 from astrbot.core.utils.path_util import path_Mapping
-from astrbot.core.utils.session_lock import session_lock_manager

 from ..context import PipelineContext, call_event_hook
 from ..stage import Stage, register_stage
@@ -118,7 +117,9 @@ class RespondStage(Stage):
        if not self.enable_seg:
            return False

-        if self.only_llm_result and not event.get_result().is_llm_result():
+        if (result := event.get_result()) is None:
+            return False
+        if self.only_llm_result and result.is_llm_result():
            return False

        if event.get_platform_name() in [
@@ -157,7 +158,11 @@ class RespondStage(Stage):
        result = event.get_result()
        if result is None:
            return
+        if event.get_extra("_streaming_finished", False):
+            # prevent some plugin make result content type to LLM_RESULT after streaming finished, lead to send again
+            return
        if result.result_content_type == ResultContentType.STREAMING_FINISH:
+            event.set_extra("_streaming_finished", True)
            return

        logger.info(
@@ -169,12 +174,15 @@ class RespondStage(Stage):
                logger.warning("async_stream 为空，跳过发送。")
                return
            # 流式结果直接交付平台适配器处理
-            use_fallback = self.config.get("provider_settings", {}).get(
-                "streaming_segmented",
-                False,
+            realtime_segmenting = (
+                self.config.get("provider_settings", {}).get(
+                    "unsupported_streaming_strategy",
+                    "realtime_segmenting",
+                )
+                == "realtime_segmenting"
            )
            logger.info(f"应用流式输出({event.get_platform_id()})")
-            await event.send_streaming(result.async_stream, use_fallback)
+            await event.send_streaming(result.async_stream, realtime_segmenting)
            return
        if len(result.chain) > 0:
            # 检查路径映射
@@ -183,7 +191,7 @@ class RespondStage(Stage):
                    if isinstance(component, Comp.File) and component.file:
                        # 支持 File 消息段的路径映射。
                        component.file = path_Mapping(mappings, component.file)
-                        event.get_result().chain[idx] = component
+                        result.chain[idx] = component

            # 检查消息链是否为空
            try:
@@ -218,21 +226,20 @@ class RespondStage(Stage):
                        f"实际消息链为空, 跳过发送阶段。header_chain: {header_comps}, actual_chain: {result.chain}",
                    )
                    return
-                async with session_lock_manager.acquire_lock(event.unified_msg_origin):
-                    for comp in result.chain:
-                        i = await self._calc_comp_interval(comp)
-                        await asyncio.sleep(i)
-                        try:
-                            if comp.type in need_separately:
-                                await event.send(MessageChain([comp]))
-                            else:
-                                await event.send(MessageChain([*header_comps, comp]))
-                                header_comps.clear()
-                        except Exception as e:
-                            logger.error(
-                                f"发送消息链失败: chain = {MessageChain([comp])}, error = {e}",
-                                exc_info=True,
-                            )
+                for comp in result.chain:
+                    i = await self._calc_comp_interval(comp)
+                    await asyncio.sleep(i)
+                    try:
+                        if comp.type in need_separately:
+                            await event.send(MessageChain([comp]))
+                        else:
+                            await event.send(MessageChain([*header_comps, comp]))
+                            header_comps.clear()
+                    except Exception as e:
+                        logger.error(
+                            f"发送消息链失败: chain = {MessageChain([comp])}, error = {e}",
+                            exc_info=True,
+                        )
            else:
                if all(
                    comp.type in {ComponentType.Reply, ComponentType.At}
@@ -1,3 +1,4 @@
+import random
 import re
 import time
 import traceback
@@ -6,6 +7,7 @@ from collections.abc import AsyncGenerator
 from astrbot.core import file_token_service, html_renderer, logger
 from astrbot.core.message.components import At, File, Image, Node, Plain, Record, Reply
 from astrbot.core.message.message_event_result import ResultContentType
+from astrbot.core.pipeline.content_safety_check.stage import ContentSafetyCheckStage
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.platform.message_type import MessageType
 from astrbot.core.star.session_llm_manager import SessionServiceManager
@@ -41,6 +43,18 @@ class ResultDecorateStage(Stage):
            "forward_threshold"
        ]

+        trigger_probability = ctx.astrbot_config["provider_tts_settings"].get(
+            "trigger_probability",
+            1,
+        )
+        try:
+            self.tts_trigger_probability = max(
+                0.0,
+                min(float(trigger_probability), 1.0),
+            )
+        except (TypeError, ValueError):
+            self.tts_trigger_probability = 1.0
+
        # 分段回复
        self.words_count_threshold = int(
            ctx.astrbot_config["platform_settings"]["segmented_reply"][
@@ -53,7 +67,22 @@ class ResultDecorateStage(Stage):
        self.only_llm_result = ctx.astrbot_config["platform_settings"][
            "segmented_reply"
        ]["only_llm_result"]
+        self.split_mode = ctx.astrbot_config["platform_settings"][
+            "segmented_reply"
+        ].get("split_mode", "regex")
        self.regex = ctx.astrbot_config["platform_settings"]["segmented_reply"]["regex"]
+        self.split_words = ctx.astrbot_config["platform_settings"][
+            "segmented_reply"
+        ].get("split_words", ["。", "？", "！", "~", "…"])
+        if self.split_words:
+            escaped_words = sorted(
+                [re.escape(word) for word in self.split_words], key=len, reverse=True
+            )
+            self.split_words_pattern = re.compile(
+                f"(.*?({'|'.join(escaped_words)})|.+$)", re.DOTALL
+            )
+        else:
+            self.split_words_pattern = None
        self.content_cleanup_rule = ctx.astrbot_config["platform_settings"][
            "segmented_reply"
        ]["content_cleanup_rule"]
@@ -69,6 +98,28 @@ class ResultDecorateStage(Stage):
                    self.content_safe_check_stage = stage_cls()
                    await self.content_safe_check_stage.initialize(ctx)

+    def _split_text_by_words(self, text: str) -> list[str]:
+        """使用分段词列表分段文本"""
+        if not self.split_words_pattern:
+            return [text]
+
+        segments = self.split_words_pattern.findall(text)
+        result = []
+        for seg in segments:
+            if isinstance(seg, tuple):
+                content = seg[0]
+                if not isinstance(content, str):
+                    continue
+                for word in self.split_words:
+                    if content.endswith(word):
+                        content = content[: -len(word)]
+                        break
+                if content.strip():
+                    result.append(content)
+            elif seg and seg.strip():
+                result.append(seg)
+        return result if result else [text]
+
    async def process(
        self,
        event: AstrMessageEvent,
@@ -93,11 +144,13 @@ class ResultDecorateStage(Stage):
            for comp in result.chain:
                if isinstance(comp, Plain):
                    text += comp.text
-            async for _ in self.content_safe_check_stage.process(
-                event,
-                check_text=text,
-            ):
-                yield
+
+            if isinstance(self.content_safe_check_stage, ContentSafetyCheckStage):
+                async for _ in self.content_safe_check_stage.process(
+                    event,
+                    check_text=text,
+                ):
+                    yield

        # 发送消息前事件钩子
        handlers = star_handlers_registry.get_handlers_by_event_type(
@@ -114,7 +167,8 @@ class ResultDecorateStage(Stage):
                        "启用流式输出时，依赖发送消息前事件钩子的插件可能无法正常工作",
                    )
                await handler.handler(event)
-                if event.get_result() is None or not event.get_result().chain:
+
+                if (result := event.get_result()) is None or not result.chain:
                    logger.debug(
                        f"hook(on_decorating_result) -> {star_map[handler.handler_module_path].name} - {handler.handler_name} 将消息结果清空。",
                    )
@@ -161,11 +215,27 @@ class ResultDecorateStage(Stage):
                                # 不分段回复
                                new_chain.append(comp)
                                continue
-                            split_response = re.findall(
-                                self.regex,
-                                comp.text,
-                                re.DOTALL | re.MULTILINE,
-                            )
+
+                            # 根据 split_mode 选择分段方式
+                            if self.split_mode == "words":
+                                split_response = self._split_text_by_words(comp.text)
+                            else:  # regex 模式
+                                try:
+                                    split_response = re.findall(
+                                        self.regex,
+                                        comp.text,
+                                        re.DOTALL | re.MULTILINE,
+                                    )
+                                except re.error:
+                                    logger.error(
+                                        f"分段回复正则表达式错误，使用默认分段方式: {traceback.format_exc()}",
+                                    )
+                                    split_response = re.findall(
+                                        r".*?[。？！~…]+|.+$",
+                                        comp.text,
+                                        re.DOTALL | re.MULTILINE,
+                                    )
+
                            if not split_response:
                                new_chain.append(comp)
                                continue
@@ -189,7 +259,14 @@ class ResultDecorateStage(Stage):
                and result.is_llm_result()
                and SessionServiceManager.should_process_tts_request(event)
            ):
-                if not tts_provider:
+                should_tts = self.tts_trigger_probability >= 1.0 or (
+                    self.tts_trigger_probability > 0.0
+                    and random.random() <= self.tts_trigger_probability
+                )
+
+                if not should_tts:
+                    logger.debug("跳过 TTS：触发概率未命中。")
+                elif not tts_provider:
                    logger.warning(
                        f"会话 {event.unified_msg_origin} 未配置文本转语音模型。",
                    )
@@ -2,6 +2,10 @@ from collections.abc import AsyncGenerator

 from astrbot.core import logger
 from astrbot.core.platform import AstrMessageEvent
+from astrbot.core.platform.sources.webchat.webchat_event import WebChatMessageEvent
+from astrbot.core.platform.sources.wecom_ai_bot.wecomai_event import (
+    WecomAIBotMessageEvent,
+)

 from . import STAGES_ORDER
 from .context import PipelineContext
@@ -78,7 +82,7 @@ class PipelineScheduler:
        await self._process_stages(event)

        # 如果没有发送操作, 则发送一个空消息, 以便于后续的处理
-        if event.get_platform_name() in ["webchat", "wecom_ai_bot"]:
+        if isinstance(event, (WebChatMessageEvent, WecomAIBotMessageEvent)):
            await event.send(None)

        logger.debug("pipeline 执行完毕。")
@@ -50,6 +50,9 @@ class WakingCheckStage(Stage):
            "ignore_at_all",
            False,
        )
+        self.disable_builtin_commands = self.ctx.astrbot_config.get(
+            "disable_builtin_commands", False
+        )

    async def process(
        self,
@@ -131,6 +134,13 @@ class WakingCheckStage(Stage):
            EventType.AdapterMessageEvent,
            plugins_name=event.plugins_name,
        ):
+            if (
+                self.disable_builtin_commands
+                and handler.handler_module_path == "packages.builtin_commands.main"
+            ):
+                logger.debug("skipping builtin command")
+                continue
+
            # filter 需满足 AND 逻辑关系
            passed = True
            permission_not_pass = False
@@ -153,7 +153,9 @@ class AstrMessageEvent(abc.ABC):

    def get_sender_name(self) -> str:
        """获取消息发送者的名称。(可能会返回空字符串)"""
-        return self.message_obj.sender.nickname
+        if isinstance(self.message_obj.sender.nickname, str):
+            return self.message_obj.sender.nickname
+        return ""

    def set_extra(self, key, value):
        """设置额外的信息。"""
@@ -270,7 +272,7 @@ class AstrMessageEvent(abc.ABC):
        """
        self.call_llm = call_llm

-    def get_result(self) -> MessageEventResult:
+    def get_result(self) -> MessageEventResult | None:
        """获取消息事件的结果。"""
        return self._result

@@ -320,7 +322,7 @@ class AstrMessageEvent(abc.ABC):
        self,
        prompt: str,
        func_tool_manager=None,
-        session_id: str = None,
+        session_id: str = "",
        image_urls: list[str] | None = None,
        contexts: list | None = None,
        system_prompt: str = "",
@@ -54,7 +54,7 @@ class AstrBotMessage:
    self_id: str  # 机器人的识别id
    session_id: str  # 会话id。取决于 unique_session 的设置。
    message_id: str  # 消息id
-    group: Group  # 群组
+    group: Group | None  # 群组
    sender: MessageMember  # 发送者
    message: list[BaseMessageComponent]  # 消息链使用 Nakuru 的消息链格式
    message_str: str  # 最直观的纯文本消息字符串
@@ -78,7 +78,7 @@ class AstrBotMessage:
        return ""

    @group_id.setter
-    def group_id(self, value: str):
+    def group_id(self, value: str | None):
        """设置 group_id"""
        if value:
            if self.group:
@@ -5,8 +5,9 @@ from asyncio import Queue
 from astrbot.core import logger
 from astrbot.core.config.astrbot_config import AstrBotConfig
 from astrbot.core.star.star_handler import EventType, star_handlers_registry, star_map
+from astrbot.core.utils.webhook_utils import ensure_platform_webhook_config

-from .platform import Platform
+from .platform import Platform, PlatformStatus
 from .register import platform_cls_map
 from .sources.webchat.webchat_adapter import WebChatAdapter

@@ -16,8 +17,9 @@ class PlatformManager:
        self.platform_insts: list[Platform] = []
        """加载的 Platform 的实例"""

-        self._inst_map = {}
+        self._inst_map: dict[str, dict] = {}

+        self.astrbot_config = config
        self.platforms_config = config["platform"]
        self.settings = config["platform_settings"]
        """NOTE: 这里是 default 的配置文件，以保证最大的兼容性；
@@ -29,6 +31,8 @@ class PlatformManager:
        """初始化所有平台适配器"""
        for platform in self.platforms_config:
            try:
+                if ensure_platform_webhook_config(platform):
+                    self.astrbot_config.save_config()
                await self.load_platform(platform)
            except Exception as e:
                logger.error(f"初始化 {platform} 平台适配器失败: {e}")
@@ -37,7 +41,10 @@ class PlatformManager:
        webchat_inst = WebChatAdapter({}, self.settings, self.event_queue)
        self.platform_insts.append(webchat_inst)
        asyncio.create_task(
-            self._task_wrapper(asyncio.create_task(webchat_inst.run(), name="webchat")),
+            self._task_wrapper(
+                asyncio.create_task(webchat_inst.run(), name="webchat"),
+                platform=webchat_inst,
+            ),
        )

    async def load_platform(self, platform_config: dict):
@@ -107,7 +114,7 @@ class PlatformManager:
                    )
        except (ImportError, ModuleNotFoundError) as e:
            logger.error(
-                f"加载平台适配器 {platform_config['type']} 失败，原因：{e}。请检查依赖库是否安装。提示：可以在 管理面板->控制台->安装Pip库 中安装依赖库。",
+                f"加载平台适配器 {platform_config['type']} 失败，原因：{e}。请检查依赖库是否安装。提示：可以在 管理面板->平台日志->安装Pip库 中安装依赖库。",
            )
        except Exception as e:
            logger.error(f"加载平台适配器 {platform_config['type']} 失败，原因：{e}。")
@@ -131,6 +138,7 @@ class PlatformManager:
                    inst.run(),
                    name=f"platform_{platform_config['type']}_{platform_config['id']}",
                ),
+                platform=inst,
            ),
        )
        handlers = star_handlers_registry.get_handlers_by_event_type(
@@ -145,17 +153,28 @@ class PlatformManager:
            except Exception:
                logger.error(traceback.format_exc())

-    async def _task_wrapper(self, task: asyncio.Task):
+    async def _task_wrapper(self, task: asyncio.Task, platform: Platform | None = None):
+        # 设置平台状态为运行中
+        if platform:
+            platform.status = PlatformStatus.RUNNING
+
        try:
            await task
        except asyncio.CancelledError:
-            pass
+            if platform:
+                platform.status = PlatformStatus.STOPPED
        except Exception as e:
+            error_msg = str(e)
+            tb_str = traceback.format_exc()
            logger.error(f"------- 任务 {task.get_name()} 发生错误: {e}")
-            for line in traceback.format_exc().split("\n"):
+            for line in tb_str.split("\n"):
                logger.error(f"|    {line}")
            logger.error("-------")

+            # 记录错误到平台实例
+            if platform:
+                platform.record_error(error_msg, tb_str)
+
    async def reload(self, platform_config: dict):
        await self.terminate_platform(platform_config["id"])
        if platform_config["enable"]:
@@ -172,9 +191,9 @@ class PlatformManager:
            logger.info(f"正在尝试终止 {platform_id} 平台适配器 ...")

            # client_id = self._inst_map.pop(platform_id, None)
-            info = self._inst_map.pop(platform_id, None)
+            info = self._inst_map.pop(platform_id)
            client_id = info["client_id"]
-            inst = info["inst"]
+            inst: Platform = info["inst"]
            try:
                self.platform_insts.remove(
                    next(
@@ -196,3 +215,46 @@ class PlatformManager:

    def get_insts(self):
        return self.platform_insts
+
+    def get_all_stats(self) -> dict:
+        """获取所有平台的统计信息
+
+        Returns:
+            包含所有平台统计信息的字典
+        """
+        stats_list = []
+        total_errors = 0
+        running_count = 0
+        error_count = 0
+
+        for inst in self.platform_insts:
+            try:
+                stat = inst.get_stats()
+                stats_list.append(stat)
+                total_errors += stat.get("error_count", 0)
+                if stat.get("status") == PlatformStatus.RUNNING.value:
+                    running_count += 1
+                elif stat.get("status") == PlatformStatus.ERROR.value:
+                    error_count += 1
+            except Exception as e:
+                # 如果获取统计信息失败，记录基本信息
+                logger.warning(f"获取平台统计信息失败: {e}")
+                stats_list.append(
+                    {
+                        "id": getattr(inst, "config", {}).get("id", "unknown"),
+                        "type": "unknown",
+                        "status": "unknown",
+                        "error_count": 0,
+                        "last_error": None,
+                    }
+                )
+
+        return {
+            "platforms": stats_list,
+            "summary": {
+                "total": len(stats_list),
+                "running": running_count,
+                "error": error_count,
+                "total_errors": total_errors,
+            },
+        }
@@ -1,7 +1,10 @@
 import abc
 import uuid
 from asyncio import Queue
-from collections.abc import Awaitable
+from collections.abc import Coroutine
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
 from typing import Any

 from astrbot.core.message.message_event_result import MessageChain
@@ -12,15 +15,100 @@ from .message_session import MessageSesion
 from .platform_metadata import PlatformMetadata


+class PlatformStatus(Enum):
+    """平台运行状态"""
+
+    PENDING = "pending"  # 待启动
+    RUNNING = "running"  # 运行中
+    ERROR = "error"  # 发生错误
+    STOPPED = "stopped"  # 已停止
+
+
+@dataclass
+class PlatformError:
+    """平台错误信息"""
+
+    message: str
+    timestamp: datetime = field(default_factory=datetime.now)
+    traceback: str | None = None
+
+
 class Platform(abc.ABC):
-    def __init__(self, event_queue: Queue):
+    def __init__(self, config: dict, event_queue: Queue):
        super().__init__()
+        # 平台配置
+        self.config = config
        # 维护了消息平台的事件队列，EventBus 会从这里取出事件并处理。
        self._event_queue = event_queue
        self.client_self_id = uuid.uuid4().hex

+        # 平台运行状态
+        self._status: PlatformStatus = PlatformStatus.PENDING
+        self._errors: list[PlatformError] = []
+        self._started_at: datetime | None = None
+
+    @property
+    def status(self) -> PlatformStatus:
+        """获取平台运行状态"""
+        return self._status
+
+    @status.setter
+    def status(self, value: PlatformStatus):
+        """设置平台运行状态"""
+        self._status = value
+        if value == PlatformStatus.RUNNING and self._started_at is None:
+            self._started_at = datetime.now()
+
+    @property
+    def errors(self) -> list[PlatformError]:
+        """获取错误列表"""
+        return self._errors
+
+    @property
+    def last_error(self) -> PlatformError | None:
+        """获取最近的错误"""
+        return self._errors[-1] if self._errors else None
+
+    def record_error(self, message: str, traceback_str: str | None = None):
+        """记录一个错误"""
+        self._errors.append(PlatformError(message=message, traceback=traceback_str))
+        self._status = PlatformStatus.ERROR
+
+    def clear_errors(self):
+        """清除错误记录"""
+        self._errors.clear()
+        if self._status == PlatformStatus.ERROR:
+            self._status = PlatformStatus.RUNNING
+
+    def unified_webhook(self) -> bool:
+        """是否正在使用统一 Webhook 模式"""
+        return bool(
+            self.config.get("unified_webhook_mode", False)
+            and self.config.get("webhook_uuid")
+        )
+
+    def get_stats(self) -> dict:
+        """获取平台统计信息"""
+        meta = self.meta()
+        return {
+            "id": meta.id or self.config.get("id"),
+            "type": meta.name,
+            "display_name": meta.adapter_display_name or meta.name,
+            "status": self._status.value,
+            "started_at": self._started_at.isoformat() if self._started_at else None,
+            "error_count": len(self._errors),
+            "last_error": {
+                "message": self.last_error.message,
+                "timestamp": self.last_error.timestamp.isoformat(),
+                "traceback": self.last_error.traceback,
+            }
+            if self.last_error
+            else None,
+            "unified_webhook": self.unified_webhook(),
+        }
+
    @abc.abstractmethod
-    def run(self) -> Awaitable[Any]:
+    def run(self) -> Coroutine[Any, Any, None]:
        """得到一个平台的运行实例，需要返回一个协程对象。"""
        raise NotImplementedError

@@ -36,7 +124,7 @@ class Platform(abc.ABC):
        self,
        session: MessageSesion,
        message_chain: MessageChain,
-    ) -> Awaitable[Any]:
+    ) -> None:
        """通过会话发送消息。该方法旨在让插件能够直接通过**可持久化的会话数据**发送消息，而不需要保存 event 对象。

        异步方法。
@@ -49,3 +137,20 @@ class Platform(abc.ABC):

    def get_client(self):
        """获取平台的客户端对象。"""
+
+    async def webhook_callback(self, request: Any) -> Any:
+        """统一 Webhook 回调入口。
+
+        支持统一 Webhook 模式的平台需要实现此方法。
+        当 Dashboard 收到 /api/platform/webhook/{uuid} 请求时，会调用此方法。
+
+        Args:
+            request: Quart 请求对象
+
+        Returns:
+            响应内容，格式取决于具体平台的要求
+
+        Raises:
+            NotImplementedError: 平台未实现统一 Webhook 模式
+        """
+        raise NotImplementedError(f"平台 {self.meta().name} 未实现统一 Webhook 模式")
@@ -7,7 +7,7 @@ class PlatformMetadata:
    """平台的名称，即平台的类型，如 aiocqhttp, discord, slack"""
    description: str
    """平台的描述"""
-    id: str | None = None
+    id: str
    """平台的唯一标识符，用于配置中识别特定平台"""

    default_config_tmpl: dict | None = None
@@ -16,3 +16,6 @@ class PlatformMetadata:
    """显示在 WebUI 配置页中的平台名称，如空则是 name"""
    logo_path: str | None = None
    """平台适配器的 logo 文件路径（相对于插件目录）"""
+
+    support_streaming_message: bool = True
+    """平台是否支持真实流式传输"""
@@ -14,6 +14,7 @@ def register_platform_adapter(
    default_config_tmpl: dict | None = None,
    adapter_display_name: str | None = None,
    logo_path: str | None = None,
+    support_streaming_message: bool = True,
 ):
    """用于注册平台适配器的带参装饰器。

@@ -39,9 +40,11 @@ def register_platform_adapter(
        pm = PlatformMetadata(
            name=adapter_name,
            description=desc,
+            id=adapter_name,
            default_config_tmpl=default_config_tmpl,
            adapter_display_name=adapter_display_name,
            logo_path=logo_path,
+            support_streaming_message=support_streaming_message,
        )
        platform_registry.append(pm)
        platform_cls_map[adapter_name] = cls
@@ -70,16 +70,18 @@ class AiocqhttpMessageEvent(AstrMessageEvent):
        bot: CQHttp,
        event: Event | None,
        is_group: bool,
-        session_id: str,
+        session_id: str | None,
        messages: list[dict],
    ):
        # session_id 必须是纯数字字符串
-        session_id = int(session_id) if session_id.isdigit() else None
+        session_id_int = (
+            int(session_id) if session_id and session_id.isdigit() else None
+        )

-        if is_group and isinstance(session_id, int):
-            await bot.send_group_msg(group_id=session_id, message=messages)
-        elif not is_group and isinstance(session_id, int):
-            await bot.send_private_msg(user_id=session_id, message=messages)
+        if is_group and isinstance(session_id_int, int):
+            await bot.send_group_msg(group_id=session_id_int, message=messages)
+        elif not is_group and isinstance(session_id_int, int):
+            await bot.send_private_msg(user_id=session_id_int, message=messages)
        elif isinstance(event, Event):  # 最后兜底
            await bot.send(event=event, message=messages)
        else:
@@ -4,7 +4,7 @@ import logging
 import time
 import uuid
 from collections.abc import Awaitable
-from typing import Any
+from typing import Any, cast

 from aiocqhttp import CQHttp, Event
 from aiocqhttp.exceptions import ActionFailed
@@ -29,6 +29,7 @@ from .aiocqhttp_message_event import AiocqhttpMessageEvent
@register_platform_adapter(
    "aiocqhttp",
    "适用于 OneBot V11 标准的消息平台适配器，支持反向 WebSockets。",
+    support_streaming_message=False,
 )
 class AiocqhttpAdapter(Platform):
    def __init__(
@@ -37,9 +38,8 @@ class AiocqhttpAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
+        super().__init__(platform_config, event_queue)

-        self.config = platform_config
        self.settings = platform_settings
        self.unique_session = platform_settings["unique_session"]
        self.host = platform_config["ws_reverse_host"]
@@ -48,7 +48,8 @@ class AiocqhttpAdapter(Platform):
        self.metadata = PlatformMetadata(
            name="aiocqhttp",
            description="适用于 OneBot 标准的消息平台适配器，支持反向 WebSockets。",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
+            support_streaming_message=False,
        )

        self.bot = CQHttp(
@@ -126,7 +127,9 @@ class AiocqhttpAdapter(Platform):
        """OneBot V11 请求类事件"""
        abm = AstrBotMessage()
        abm.self_id = str(event.self_id)
-        abm.sender = MessageMember(user_id=str(event.user_id), nickname=event.user_id)
+        abm.sender = MessageMember(
+            user_id=str(event.user_id), nickname=str(event.user_id)
+        )
        abm.type = MessageType.OTHER_MESSAGE
        if event.get("group_id"):
            abm.type = MessageType.GROUP_MESSAGE
@@ -152,7 +155,9 @@ class AiocqhttpAdapter(Platform):
        """OneBot V11 通知类事件"""
        abm = AstrBotMessage()
        abm.self_id = str(event.self_id)
-        abm.sender = MessageMember(user_id=str(event.user_id), nickname=event.user_id)
+        abm.sender = MessageMember(
+            user_id=str(event.user_id), nickname=str(event.user_id)
+        )
        abm.type = MessageType.OTHER_MESSAGE
        if event.get("group_id"):
            abm.group_id = str(event.group_id)
@@ -191,6 +196,7 @@ class AiocqhttpAdapter(Platform):
        @param event: 事件对象
        @param get_reply: 是否获取回复消息。这个参数是为了防止多个回复嵌套。
        """
+        assert event.sender is not None
        abm = AstrBotMessage()
        abm.self_id = str(event.self_id)
        abm.sender = MessageMember(
@@ -200,6 +206,7 @@ class AiocqhttpAdapter(Platform):
        if event["message_type"] == "group":
            abm.type = MessageType.GROUP_MESSAGE
            abm.group_id = str(event.group_id)
+            abm.group = Group(str(event.group_id))
            abm.group.group_name = event.get("group_name", "N/A")
        elif event["message_type"] == "private":
            abm.type = MessageType.FRIEND_MESSAGE
@@ -225,7 +232,7 @@ class AiocqhttpAdapter(Platform):
                await self.bot.send(event, err)
            except BaseException as e:
                logger.error(f"回复消息失败: {e}")
-            return None
+            raise ValueError(err)

        # 按消息段类型类型适配
        for t, m_group in itertools.groupby(event.message, key=lambda x: x["type"]):
@@ -244,7 +251,13 @@ class AiocqhttpAdapter(Platform):
                    if m["data"].get("url") and m["data"].get("url").startswith("http"):
                        # Lagrange
                        logger.info("guessing lagrange")
-                        file_name = m["data"].get("file_name", "file")
+                        # 检查多个可能的文件名字段
+                        file_name = (
+                            m["data"].get("file_name", "")
+                            or m["data"].get("name", "")
+                            or m["data"].get("file", "")
+                            or "file"
+                        )
                        abm.message.append(File(name=file_name, url=m["data"]["url"]))
                    else:
                        try:
@@ -263,7 +276,14 @@ class AiocqhttpAdapter(Platform):
                                )
                            if ret and "url" in ret:
                                file_url = ret["url"]  # https
-                                a = File(name="", url=file_url)
+                                # 优先从 API 返回值获取文件名，其次从原始消息数据获取
+                                file_name = (
+                                    ret.get("file_name", "")
+                                    or ret.get("name", "")
+                                    or m["data"].get("file", "")
+                                    or m["data"].get("file_name", "")
+                                )
+                                a = File(name=file_name, url=file_url)
                                abm.message.append(a)
                            else:
                                logger.error(f"获取文件失败: {ret}")
@@ -401,7 +421,7 @@ class AiocqhttpAdapter(Platform):

    async def shutdown_trigger_placeholder(self):
        await self.shutdown_event.wait()
-        logger.info("aiocqhttp 适配器已被优雅地关闭")
+        logger.info("aiocqhttp 适配器已被关闭")

    def meta(self) -> PlatformMetadata:
        return self.metadata
@@ -2,6 +2,7 @@ import asyncio
 import os
 import threading
 import uuid
+from typing import cast

 import aiohttp
 import dingtalk_stream
@@ -37,7 +38,9 @@ class MyEventHandler(dingtalk_stream.EventHandler):
        return AckMessage.STATUS_OK, "OK"


-@register_platform_adapter("dingtalk", "钉钉机器人官方 API 适配器")
+@register_platform_adapter(
+    "dingtalk", "钉钉机器人官方 API 适配器", support_streaming_message=False
+)
 class DingtalkPlatformAdapter(Platform):
    def __init__(
        self,
@@ -45,21 +48,21 @@ class DingtalkPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)

        self.unique_session = platform_settings["unique_session"]

        self.client_id = platform_config["client_id"]
        self.client_secret = platform_config["client_secret"]

+        outer_self = self
+
        class AstrCallbackClient(dingtalk_stream.ChatbotHandler):
-            async def process(self_, message: dingtalk_stream.CallbackMessage):
+            async def process(self, message: dingtalk_stream.CallbackMessage):
                logger.debug(f"dingtalk: {message.data}")
                im = dingtalk_stream.ChatbotMessage.from_dict(message.data)
-                abm = await self.convert_msg(im)
-                await self.handle_msg(abm)
+                abm = await outer_self.convert_msg(im)
+                await outer_self.handle_msg(abm)

                return AckMessage.STATUS_OK, "OK"

@@ -73,6 +76,15 @@ class DingtalkPlatformAdapter(Platform):
            self.client,
        )
        self.client_ = client  # 用于 websockets 的 client
+        self._shutdown_event: threading.Event | None = None
+
+    def _id_to_sid(self, dingtalk_id: str | None) -> str:
+        if not dingtalk_id:
+            return dingtalk_id or "unknown"
+        prefix = "$:LWCP_v1:$"
+        if dingtalk_id.startswith(prefix):
+            return dingtalk_id[len(prefix) :]
+        return dingtalk_id or "unknown"

    async def send_by_session(
        self,
@@ -85,7 +97,8 @@ class DingtalkPlatformAdapter(Platform):
        return PlatformMetadata(
            name="dingtalk",
            description="钉钉机器人官方 API 适配器",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
+            support_streaming_message=False,
        )

    async def convert_msg(
@@ -95,26 +108,26 @@ class DingtalkPlatformAdapter(Platform):
        abm = AstrBotMessage()
        abm.message = []
        abm.message_str = ""
-        abm.timestamp = int(message.create_at / 1000)
+        abm.timestamp = int(cast(int, message.create_at) / 1000)
        abm.type = (
            MessageType.GROUP_MESSAGE
            if message.conversation_type == "2"
            else MessageType.FRIEND_MESSAGE
        )
        abm.sender = MessageMember(
-            user_id=message.sender_id,
+            user_id=self._id_to_sid(message.sender_id),
            nickname=message.sender_nick,
        )
-        abm.self_id = message.chatbot_user_id
-        abm.message_id = message.message_id
+        abm.self_id = self._id_to_sid(message.chatbot_user_id)
+        abm.message_id = cast(str, message.message_id)
        abm.raw_message = message

        if abm.type == MessageType.GROUP_MESSAGE:
            # 处理所有被 @ 的用户（包括机器人自己，因 at_users 已包含）
            if message.at_users:
                for user in message.at_users:
-                    if user.dingtalk_id:
-                        abm.message.append(At(qq=user.dingtalk_id))
+                    if id := self._id_to_sid(user.dingtalk_id):
+                        abm.message.append(At(qq=id))
            abm.group_id = message.conversation_id
            if self.unique_session:
                abm.session_id = abm.sender.user_id
@@ -123,14 +136,16 @@ class DingtalkPlatformAdapter(Platform):
        else:
            abm.session_id = abm.sender.user_id

-        message_type: str = message.message_type
+        message_type: str = cast(str, message.message_type)
        match message_type:
            case "text":
                abm.message_str = message.text.content.strip()
                abm.message.append(Plain(abm.message_str))
            case "richText":
-                rtc: dingtalk_stream.RichTextContent = message.rich_text_content
-                contents: list[dict] = rtc.rich_text_list
+                rtc: dingtalk_stream.RichTextContent = cast(
+                    dingtalk_stream.RichTextContent, message.rich_text_content
+                )
+                contents: list[dict] = cast(list[dict], rtc.rich_text_list)
                for content in contents:
                    plains = ""
                    if "text" in content:
@@ -139,7 +154,7 @@ class DingtalkPlatformAdapter(Platform):
                    elif "type" in content and content["type"] == "picture":
                        f_path = await self.download_ding_file(
                            content["downloadCode"],
-                            message.robot_code,
+                            cast(str, message.robot_code),
                            "jpg",
                        )
                        abm.message.append(Image.fromFileSystem(f_path))
@@ -184,7 +199,7 @@ class DingtalkPlatformAdapter(Platform):
                logger.error(
                    f"下载钉钉文件失败: {resp.status}, {await resp.text()}",
                )
-                return None
+                return ""
            resp_data = await resp.json()
            download_url = resp_data["data"]["downloadUrl"]
            await download_file(download_url, f_path)
@@ -204,7 +219,7 @@ class DingtalkPlatformAdapter(Platform):
                    logger.error(
                        f"获取钉钉机器人 access_token 失败: {resp.status}, {await resp.text()}",
                    )
-                    return None
+                    return ""
                return (await resp.json())["data"]["accessToken"]

    async def handle_msg(self, abm: AstrBotMessage):
@@ -230,7 +245,7 @@ class DingtalkPlatformAdapter(Platform):
                    task.result()
            except Exception as e:
                if "Graceful shutdown" in str(e):
-                    logger.info("钉钉适配器已被优雅地关闭")
+                    logger.info("钉钉适配器已被关闭")
                    return
                logger.error(f"钉钉机器人启动失败: {e}")

@@ -239,11 +254,13 @@ class DingtalkPlatformAdapter(Platform):

    async def terminate(self):
        def monkey_patch_close():
-            raise Exception("Graceful shutdown")
+            raise KeyboardInterrupt("Graceful shutdown")

-        self.client_.open_connection = monkey_patch_close
-        await self.client_.websocket.close(code=1000, reason="Graceful shutdown")
-        self._shutdown_event.set()
+        if self.client_.websocket is not None:
+            self.client_.open_connection = monkey_patch_close
+            await self.client_.websocket.close(code=1000, reason="Graceful shutdown")
+        if self._shutdown_event is not None:
+            self._shutdown_event.set()

    def get_client(self):
        return self.client
@@ -1,4 +1,5 @@
 import asyncio
+from typing import cast

 import dingtalk_stream

@@ -32,7 +33,7 @@ class DingtalkMessageEvent(AstrMessageEvent):
                    client.reply_markdown,
                    segment.text,
                    segment.text,
-                    self.message_obj.raw_message,
+                    cast(dingtalk_stream.ChatbotMessage, self.message_obj.raw_message),
                )
            elif isinstance(segment, Comp.Image):
                markdown_str = ""
@@ -53,7 +54,9 @@ class DingtalkMessageEvent(AstrMessageEvent):
                        client.reply_markdown,
                        "😄",
                        markdown_str,
-                        self.message_obj.raw_message,
+                        cast(
+                            dingtalk_stream.ChatbotMessage, self.message_obj.raw_message
+                        ),
                    )
                    logger.debug(f"send image: {ret}")

@@ -1,4 +1,5 @@
 import sys
+from collections.abc import Awaitable, Callable

 import discord

@@ -27,13 +28,16 @@ class DiscordBotClient(discord.Bot):
        super().__init__(intents=intents, proxy=proxy)

        # 回调函数
-        self.on_message_received = None
-        self.on_ready_once_callback = None
+        self.on_message_received: Callable[[dict], Awaitable[None]] | None = None
+        self.on_ready_once_callback: Callable[[], Awaitable[None]] | None = None
        self._ready_once_fired = False

-    @override
    async def on_ready(self):
        """当机器人成功连接并准备就绪时触发"""
+        if self.user is None:
+            logger.error("[Discord] 客户端未正确加载用户信息 (self.user is None)")
+            return
+
        logger.info(f"[Discord] 已作为 {self.user} (ID: {self.user.id}) 登录")
        logger.info("[Discord] 客户端已准备就绪。")

@@ -49,6 +53,9 @@ class DiscordBotClient(discord.Bot):

    def _create_message_data(self, message: discord.Message) -> dict:
        """从 discord.Message 创建数据字典"""
+        if self.user is None:
+            raise RuntimeError("Bot is not ready: self.user is None")
+
        is_mentioned = self.user in message.mentions
        return {
            "message": message,
@@ -66,6 +73,12 @@ class DiscordBotClient(discord.Bot):

    def _create_interaction_data(self, interaction: discord.Interaction) -> dict:
        """从 discord.Interaction 创建数据字典"""
+        if self.user is None:
+            raise RuntimeError("Bot is not ready: self.user is None")
+
+        if interaction.user is None:
+            raise ValueError("Interaction received without a valid user")
+
        return {
            "interaction": interaction,
            "bot_id": str(self.user.id),
@@ -80,7 +93,6 @@ class DiscordBotClient(discord.Bot):
            "type": "interaction",
        }

-    @override
    async def on_message(self, message: discord.Message):
        """当接收到消息时触发"""
        if message.author.bot:
@@ -97,8 +97,8 @@ class DiscordView(BaseMessageComponent):

    def __init__(
        self,
-        components: list[BaseMessageComponent] = None,
-        timeout: float = None,
+        components: list[BaseMessageComponent] | None = None,
+        timeout: float | None = None,
    ):
        self.components = components or []
        self.timeout = timeout
@@ -1,10 +1,10 @@
 import asyncio
 import re
 import sys
-from typing import Any
+from typing import Any, cast

 import discord
-from discord.abc import Messageable
+from discord.abc import GuildChannel, Messageable, PrivateChannel
 from discord.channel import DMChannel

 from astrbot import logger
@@ -34,7 +34,9 @@ else:


 # 注册平台适配器
-@register_platform_adapter("discord", "Discord 适配器 (基于 Pycord)")
+@register_platform_adapter(
+    "discord", "Discord 适配器 (基于 Pycord)", support_streaming_message=False
+)
 class DiscordPlatformAdapter(Platform):
    def __init__(
        self,
@@ -42,10 +44,9 @@ class DiscordPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)
        self.settings = platform_settings
-        self.client_self_id = None
+        self.client_self_id: str | None = None
        self.registered_handlers = []
        # 指令注册相关
        self.enable_command_register = self.config.get("discord_command_register", True)
@@ -61,6 +62,12 @@ class DiscordPlatformAdapter(Platform):
        message_chain: MessageChain,
    ):
        """通过会话发送消息"""
+        if self.client.user is None:
+            logger.error(
+                "[Discord] 客户端未就绪 (self.client.user is None)，无法发送消息"
+            )
+            return
+
        # 创建一个 message_obj 以便在 event 中使用
        message_obj = AstrBotMessage()
        if "_" in session.session_id:
@@ -88,7 +95,7 @@ class DiscordPlatformAdapter(Platform):
            user_id=str(self.client_self_id),
            nickname=self.client.user.display_name,
        )
-        message_obj.self_id = self.client_self_id
+        message_obj.self_id = cast(str, self.client_self_id)
        message_obj.session_id = session.session_id
        message_obj.message = message_chain.chain

@@ -109,8 +116,9 @@ class DiscordPlatformAdapter(Platform):
        return PlatformMetadata(
            "discord",
            "Discord 适配器",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
            default_config_tmpl=self.config,
+            support_streaming_message=False,
        )

    @override
@@ -158,7 +166,7 @@ class DiscordPlatformAdapter(Platform):

    def _get_message_type(
        self,
-        channel: Messageable,
+        channel: Messageable | GuildChannel | PrivateChannel,
        guild_id: int | None = None,
    ) -> MessageType:
        """根据 channel 对象和 guild_id 判断消息类型"""
@@ -168,13 +176,15 @@ class DiscordPlatformAdapter(Platform):
            return MessageType.FRIEND_MESSAGE
        return MessageType.GROUP_MESSAGE

-    def _get_channel_id(self, channel: Messageable) -> str:
+    def _get_channel_id(
+        self, channel: Messageable | GuildChannel | PrivateChannel
+    ) -> str:
        """根据 channel 对象获取ID"""
        return str(getattr(channel, "id", None))

    def _convert_message_to_abm(self, data: dict) -> AstrBotMessage:
        """将普通消息转换为 AstrBotMessage"""
-        message: discord.Message = data["message"]
+        message = data["message"]

        content = message.content

@@ -231,7 +241,7 @@ class DiscordPlatformAdapter(Platform):
                    )
        abm.message = message_chain
        abm.raw_message = message
-        abm.self_id = self.client_self_id
+        abm.self_id = cast(str, self.client_self_id)
        abm.session_id = str(message.channel.id)
        abm.message_id = str(message.id)
        return abm
@@ -252,32 +262,52 @@ class DiscordPlatformAdapter(Platform):
            interaction_followup_webhook=followup_webhook,
        )

+        if self.client.user is None:
+            logger.error(
+                "[Discord] 客户端未就绪 (self.client.user is None)，无法处理消息"
+            )
+            return
+
        # 检查是否为斜杠指令
        is_slash_command = message_event.interaction_followup_webhook is not None

+        # 1. 优先处理斜杠指令
+        if is_slash_command:
+            message_event.is_wake = True
+            message_event.is_at_or_wake_command = True
+            self.commit_event(message_event)
+            return
+
+        # 2. 处理普通消息（提及检测）
+        # 确保 raw_message 是 discord.Message 类型，以便静态检查通过
+        raw_message = message.raw_message
+        if not isinstance(raw_message, discord.Message):
+            logger.warning(
+                f"[Discord] 收到非 Message 类型的消息: {type(raw_message)}，已忽略。"
+            )
+            return
+
        # 检查是否被@（User Mention 或 Bot 拥有的 Role Mention）
        is_mention = False
+
        # User Mention
-        if (
-            self.client
-            and self.client.user
-            and hasattr(message.raw_message, "mentions")
-        ):
-            if self.client.user in message.raw_message.mentions:
-                is_mention = True
+        # 此时 Pylance 知道 raw_message 是 discord.Message，具有 mentions 属性
+        if self.client.user in raw_message.mentions:
+            is_mention = True
+
        # Role Mention（Bot 拥有的角色被提及）
-        if not is_mention and hasattr(message.raw_message, "role_mentions"):
+        if not is_mention and raw_message.role_mentions:
            bot_member = None
-            if hasattr(message.raw_message, "guild") and message.raw_message.guild:
+            if raw_message.guild:
                try:
-                    bot_member = message.raw_message.guild.get_member(
+                    bot_member = raw_message.guild.get_member(
                        self.client.user.id,
                    )
                except Exception:
                    bot_member = None
            if bot_member and hasattr(bot_member, "roles"):
                bot_roles = set(bot_member.roles)
-                mentioned_roles = set(message.raw_message.role_mentions)
+                mentioned_roles = set(raw_message.role_mentions)
                if (
                    bot_roles
                    and mentioned_roles
@@ -285,8 +315,8 @@ class DiscordPlatformAdapter(Platform):
                ):
                    is_mention = True

-        # 如果是斜杠指令或被@的消息，设置为唤醒状态
-        if is_slash_command or is_mention:
+        # 如果是被@的消息，设置为唤醒状态
+        if is_mention:
            message_event.is_wake = True
            message_event.is_at_or_wake_command = True

@@ -422,7 +452,7 @@ class DiscordPlatformAdapter(Platform):
            )
            abm.message = [Plain(text=message_str_for_filter)]
            abm.raw_message = ctx.interaction
-            abm.self_id = self.client_self_id
+            abm.self_id = cast(str, self.client_self_id)
            abm.session_id = str(ctx.channel_id)
            abm.message_id = str(ctx.interaction.id)

@@ -435,7 +465,7 @@ class DiscordPlatformAdapter(Platform):
    def _extract_command_info(
        event_filter: Any,
        handler_metadata: StarHandlerMetadata,
-    ) -> tuple[str, str, CommandFilter] | None:
+    ) -> tuple[str, str, CommandFilter | None] | None:
        """从事件过滤器中提取指令信息"""
        cmd_name = None
        # is_group = False
@@ -1,11 +1,13 @@
 import asyncio
 import base64
 import binascii
-import sys
+from collections.abc import AsyncGenerator
 from io import BytesIO
 from pathlib import Path
+from typing import cast

 import discord
+from discord.types.interactions import ComponentInteractionData

 from astrbot import logger
 from astrbot.api.event import AstrMessageEvent, MessageChain
@@ -21,11 +23,6 @@ from astrbot.api.platform import AstrBotMessage, At, PlatformMetadata
 from .client import DiscordBotClient
 from .components import DiscordEmbed, DiscordView

-if sys.version_info >= (3, 12):
-    from typing import override
-else:
-    from typing_extensions import override
-

 # 自定义Discord视图组件（兼容旧版本）
 class DiscordViewComponent(BaseMessageComponent):
@@ -49,7 +46,6 @@ class DiscordPlatformEvent(AstrMessageEvent):
        self.client = client
        self.interaction_followup_webhook = interaction_followup_webhook

-    @override
    async def send(self, message: MessageChain):
        """发送消息到Discord平台"""
        # 解析消息链为 Discord 所需的对象
@@ -91,6 +87,9 @@ class DiscordPlatformEvent(AstrMessageEvent):
                channel = await self._get_channel()
                if not channel:
                    return
+                if not isinstance(channel, discord.abc.Messageable):
+                    logger.error(f"[Discord] 频道 {channel.id} 不是可发送消息的类型")
+                    return
                await channel.send(**kwargs)

        except Exception as e:
@@ -98,7 +97,24 @@ class DiscordPlatformEvent(AstrMessageEvent):

        await super().send(message)

-    async def _get_channel(self) -> discord.abc.Messageable | None:
+    async def send_streaming(
+        self, generator: AsyncGenerator[MessageChain, None], use_fallback: bool = False
+    ):
+        buffer = None
+        async for chain in generator:
+            if not buffer:
+                buffer = chain
+            else:
+                buffer.chain.extend(chain.chain)
+        if not buffer:
+            return None
+        buffer.squash_plain()
+        await self.send(buffer)
+        return await super().send_streaming(generator, use_fallback)
+
+    async def _get_channel(
+        self,
+    ) -> discord.Thread | discord.abc.GuildChannel | discord.abc.PrivateChannel | None:
        """获取当前事件对应的频道对象"""
        try:
            channel_id = int(self.session_id)
@@ -112,7 +128,13 @@ class DiscordPlatformEvent(AstrMessageEvent):
    async def _parse_to_discord(
        self,
        message: MessageChain,
-    ) -> tuple[str, list[discord.File], discord.ui.View | None, list[discord.Embed]]:
+    ) -> tuple[
+        str,
+        list[discord.File],
+        discord.ui.View | None,
+        list[discord.Embed],
+        str | int | None,
+    ]:
        """将 MessageChain 解析为 Discord 发送所需的内容"""
        content_parts = []
        files = []
@@ -252,7 +274,9 @@ class DiscordPlatformEvent(AstrMessageEvent):
                self.message_obj.raw_message,
                "add_reaction",
            ):
-                await self.message_obj.raw_message.add_reaction(emoji)
+                await cast(discord.Message, self.message_obj.raw_message).add_reaction(
+                    emoji
+                )
        except Exception as e:
            logger.error(f"[Discord] 添加反应失败: {e}")

@@ -261,7 +285,7 @@ class DiscordPlatformEvent(AstrMessageEvent):
        return (
            hasattr(self.message_obj, "raw_message")
            and hasattr(self.message_obj.raw_message, "type")
-            and self.message_obj.raw_message.type
+            and cast(discord.Interaction, self.message_obj.raw_message).type
            == discord.InteractionType.application_command
        )

@@ -270,14 +294,18 @@ class DiscordPlatformEvent(AstrMessageEvent):
        return (
            hasattr(self.message_obj, "raw_message")
            and hasattr(self.message_obj.raw_message, "type")
-            and self.message_obj.raw_message.type == discord.InteractionType.component
+            and cast(discord.Interaction, self.message_obj.raw_message).type
+            == discord.InteractionType.component
        )

    def get_interaction_custom_id(self) -> str:
        """获取交互组件的custom_id"""
        if self.is_button_interaction():
            try:
-                return self.message_obj.raw_message.data.get("custom_id", "")
+                return cast(
+                    ComponentInteractionData,
+                    cast(discord.Interaction, self.message_obj.raw_message).data,
+                ).get("custom_id", "")
            except Exception:
                pass
        return ""
@@ -290,7 +318,9 @@ class DiscordPlatformEvent(AstrMessageEvent):
        ):
            return any(
                mention.id == int(self.message_obj.self_id)
-                for mention in self.message_obj.raw_message.mentions
+                for mention in cast(
+                    discord.Message, self.message_obj.raw_message
+                ).mentions
            )
        return False

@@ -300,5 +330,5 @@ class DiscordPlatformEvent(AstrMessageEvent):
            self.message_obj.raw_message,
            "clean_content",
        ):
-            return self.message_obj.raw_message.clean_content
+            return cast(discord.Message, self.message_obj.raw_message).clean_content
        return self.message_str
@@ -2,10 +2,17 @@ import asyncio
 import base64
 import json
 import re
+import time
 import uuid
+from typing import Any, cast

 import lark_oapi as lark
-from lark_oapi.api.im.v1 import *
+from lark_oapi.api.im.v1 import (
+    CreateMessageRequest,
+    CreateMessageRequestBody,
+    GetMessageResourceRequest,
+)
+from lark_oapi.api.im.v1.processor import P2ImMessageReceiveV1Processor

 import astrbot.api.message_components as Comp
 from astrbot import logger
@@ -18,12 +25,16 @@ from astrbot.api.platform import (
    PlatformMetadata,
 )
 from astrbot.core.platform.astr_message_event import MessageSesion
+from astrbot.core.utils.webhook_utils import log_webhook_info

 from ...register import register_platform_adapter
 from .lark_event import LarkMessageEvent
+from .server import LarkWebhookServer


-@register_platform_adapter("lark", "飞书机器人官方 API 适配器")
+@register_platform_adapter(
+    "lark", "飞书机器人官方 API 适配器", support_streaming_message=False
+)
 class LarkPlatformAdapter(Platform):
    def __init__(
        self,
@@ -31,9 +42,7 @@ class LarkPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)

        self.unique_session = platform_settings["unique_session"]

@@ -42,9 +51,13 @@ class LarkPlatformAdapter(Platform):
        self.domain = platform_config.get("domain", lark.FEISHU_DOMAIN)
        self.bot_name = platform_config.get("lark_bot_name", "astrbot")

+        # socket or webhook
+        self.connection_mode = platform_config.get("lark_connection_mode", "socket")
+
        if not self.bot_name:
            logger.warning("未设置飞书机器人名称，@ 机器人可能得不到回复。")

+        # 初始化 WebSocket 长连接相关配置
        async def on_msg_event_recv(event: lark.im.v1.P2ImMessageReceiveV1):
            await self.convert_msg(event)

@@ -57,6 +70,8 @@ class LarkPlatformAdapter(Platform):
            .build()
        )

+        self.do_v2_msg_event = do_v2_msg_event
+
        self.client = lark.ws.Client(
            app_id=self.appid,
            app_secret=self.appsecret,
@@ -66,14 +81,56 @@ class LarkPlatformAdapter(Platform):
        )

        self.lark_api = (
-            lark.Client.builder().app_id(self.appid).app_secret(self.appsecret).build()
+            lark.Client.builder()
+            .app_id(self.appid)
+            .app_secret(self.appsecret)
+            .log_level(lark.LogLevel.ERROR)
+            .domain(self.domain)
+            .build()
        )

+        self.webhook_server = None
+        if self.connection_mode == "webhook":
+            self.webhook_server = LarkWebhookServer(platform_config, event_queue)
+            self.webhook_server.set_callback(self.handle_webhook_event)
+
+        self.event_id_timestamps: dict[str, float] = {}
+
+    def _clean_expired_events(self):
+        """清理超过 30 分钟的事件记录"""
+        current_time = time.time()
+        expired_keys = [
+            event_id
+            for event_id, timestamp in self.event_id_timestamps.items()
+            if current_time - timestamp > 1800
+        ]
+        for event_id in expired_keys:
+            del self.event_id_timestamps[event_id]
+
+    def _is_duplicate_event(self, event_id: str) -> bool:
+        """检查事件是否重复
+
+        Args:
+            event_id: 事件ID
+
+        Returns:
+            True 表示重复事件，False 表示新事件
+        """
+        self._clean_expired_events()
+        if event_id in self.event_id_timestamps:
+            return True
+        self.event_id_timestamps[event_id] = time.time()
+        return False
+
    async def send_by_session(
        self,
        session: MessageSesion,
        message_chain: MessageChain,
    ):
+        if self.lark_api.im is None:
+            logger.error("[Lark] API Client im 模块未初始化，无法发送消息")
+            return
+
        res = await LarkMessageEvent._convert_to_lark(message_chain, self.lark_api)
        wrapped = {
            "zh_cn": {
@@ -114,13 +171,25 @@ class LarkPlatformAdapter(Platform):
        return PlatformMetadata(
            name="lark",
            description="飞书机器人官方 API 适配器",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
+            support_streaming_message=False,
        )

    async def convert_msg(self, event: lark.im.v1.P2ImMessageReceiveV1):
+        if event.event is None:
+            logger.debug("[Lark] 收到空事件(event.event is None)")
+            return
        message = event.event.message
+        if message is None:
+            logger.debug("[Lark] 事件中没有消息体(message is None)")
+            return
+
        abm = AstrBotMessage()
-        abm.timestamp = int(message.create_time) / 1000
+
+        if message.create_time:
+            abm.timestamp = int(message.create_time) // 1000
+        else:
+            abm.timestamp = int(time.time())
        abm.message = []
        abm.type = (
            MessageType.GROUP_MESSAGE
@@ -135,14 +204,28 @@ class LarkPlatformAdapter(Platform):
        at_list = {}
        if message.mentions:
            for m in message.mentions:
-                at_list[m.key] = Comp.At(qq=m.id.open_id, name=m.name)
-                if m.name == self.bot_name:
-                    abm.self_id = m.id.open_id
+                if m.id is None:
+                    continue
+                # 飞书 open_id 可能是 None，这里做个防护
+                open_id = m.id.open_id if m.id.open_id else ""
+                at_list[m.key] = Comp.At(qq=open_id, name=m.name)

-        content_json_b = json.loads(message.content)
+                if m.name == self.bot_name:
+                    if m.id.open_id is not None:
+                        abm.self_id = m.id.open_id
+
+        if message.content is None:
+            logger.warning("[Lark] 消息内容为空")
+            return
+
+        try:
+            content_json_b = json.loads(message.content)
+        except json.JSONDecodeError:
+            logger.error(f"[Lark] 解析消息内容失败: {message.content}")
+            return

        if message.message_type == "text":
-            message_str_raw = content_json_b["text"]  # 带有 @ 的消息
+            message_str_raw = content_json_b.get("text", "")  # 带有 @ 的消息
            at_pattern = r"(@_user_\d+)"  # 可以根据需求修改正则
            # at_users = re.findall(at_pattern, message_str_raw)
            # 拆分文本，去掉AT符号部分
@@ -167,27 +250,47 @@ class LarkPlatformAdapter(Platform):
            content_json_b = _ls
        elif message.message_type == "image":
            content_json_b = [
-                {"tag": "img", "image_key": content_json_b["image_key"], "style": []},
+                {
+                    "tag": "img",
+                    "image_key": content_json_b.get("image_key"),
+                    "style": [],
+                },
            ]

        if message.message_type in ("post", "image"):
            for comp in content_json_b:
-                if comp["tag"] == "at":
-                    abm.message.append(at_list[comp["user_id"]])
-                elif comp["tag"] == "text" and comp["text"].strip():
+                if comp.get("tag") == "at":
+                    user_id = comp.get("user_id")
+                    if user_id in at_list:
+                        abm.message.append(at_list[user_id])
+                elif comp.get("tag") == "text" and comp.get("text", "").strip():
                    abm.message.append(Comp.Plain(comp["text"].strip()))
-                elif comp["tag"] == "img":
-                    image_key = comp["image_key"]
+                elif comp.get("tag") == "img":
+                    image_key = comp.get("image_key")
+                    if not image_key:
+                        continue
+
                    request = (
                        GetMessageResourceRequest.builder()
-                        .message_id(message.message_id)
+                        .message_id(cast(str, message.message_id))
                        .file_key(image_key)
                        .type("image")
                        .build()
                    )
+
+                    if self.lark_api.im is None:
+                        logger.error("[Lark] API Client im 模块未初始化")
+                        continue
+
                    response = await self.lark_api.im.v1.message_resource.aget(request)
                    if not response.success():
                        logger.error(f"无法下载飞书图片: {image_key}")
+                        continue
+
+                    if response.file is None:
+                        logger.error(f"飞书图片响应中不包含文件流: {image_key}")
+                        continue
+
                    image_bytes = response.file.read()
                    image_base64 = base64.b64encode(image_bytes).decode()
                    abm.message.append(Comp.Image.fromBase64(image_base64))
@@ -195,6 +298,19 @@ class LarkPlatformAdapter(Platform):
        for comp in abm.message:
            if isinstance(comp, Comp.Plain):
                abm.message_str += comp.text
+
+        if message.message_id is None:
+            logger.error("[Lark] 消息缺少 message_id")
+            return
+
+        if (
+            event.event.sender is None
+            or event.event.sender.sender_id is None
+            or event.event.sender.sender_id.open_id is None
+        ):
+            logger.error("[Lark] 消息发送者信息不完整")
+            return
+
        abm.message_id = message.message_id
        abm.raw_message = message
        abm.sender = MessageMember(
@@ -226,13 +342,61 @@ class LarkPlatformAdapter(Platform):

        self._event_queue.put_nowait(event)

+    async def handle_webhook_event(self, event_data: dict):
+        """处理 Webhook 事件
+
+        Args:
+            event_data: Webhook 事件数据
+        """
+        try:
+            header = event_data.get("header", {})
+            event_id = header.get("event_id", "")
+            if event_id and self._is_duplicate_event(event_id):
+                logger.debug(f"[Lark Webhook] 跳过重复事件: {event_id}")
+                return
+            event_type = header.get("event_type", "")
+            if event_type == "im.message.receive_v1":
+                processor = P2ImMessageReceiveV1Processor(self.do_v2_msg_event)
+                data = (processor.type())(event_data)
+                processor.do(data)
+            else:
+                logger.debug(f"[Lark Webhook] 未处理的事件类型: {event_type}")
+        except Exception as e:
+            logger.error(f"[Lark Webhook] 处理事件失败: {e}", exc_info=True)
+
    async def run(self):
-        # self.client.start()
-        await self.client._connect()
+        if self.connection_mode == "webhook":
+            # Webhook 模式
+            if self.webhook_server is None:
+                logger.error("[Lark] Webhook 模式已启用，但 webhook_server 未初始化")
+                return
+
+            webhook_uuid = self.config.get("webhook_uuid")
+            if webhook_uuid:
+                log_webhook_info(f"{self.meta().id}(飞书 Webhook)", webhook_uuid)
+            else:
+                logger.warning("[Lark] Webhook 模式已启用，但未配置 webhook_uuid")
+        else:
+            # 长连接模式
+            await self.client._connect()
+
+    async def webhook_callback(self, request: Any) -> Any:
+        """统一 Webhook 回调入口"""
+        if not self.webhook_server:
+            return {"error": "Webhook server not initialized"}, 500
+
+        return await self.webhook_server.handle_callback(request)

    async def terminate(self):
-        await self.client._disconnect()
-        logger.info("飞书(Lark) 适配器已被优雅地关闭")
+        if self.connection_mode == "socket":
+            await self.client._disconnect()
+        logger.info("飞书(Lark) 适配器已关闭")

-    def get_client(self) -> lark.Client:
+    def get_client(self) -> lark.ws.Client:
        return self.client
+
+    def unified_webhook(self) -> bool:
+        return bool(
+            self.config.get("lark_connection_mode", "") == "webhook"
+            and self.config.get("webhook_uuid")
+        )
@@ -5,7 +5,15 @@ import uuid
 from io import BytesIO

 import lark_oapi as lark
-from lark_oapi.api.im.v1 import *
+from lark_oapi.api.im.v1 import (
+    CreateImageRequest,
+    CreateImageRequestBody,
+    CreateMessageReactionRequest,
+    CreateMessageReactionRequestBody,
+    Emoji,
+    ReplyMessageRequest,
+    ReplyMessageRequestBody,
+)

 from astrbot import logger
 from astrbot.api.event import AstrMessageEvent, MessageChain
@@ -44,7 +52,7 @@ class LarkMessageEvent(AstrMessageEvent):
                    file_path = comp.file.replace("file:///", "")
                elif comp.file and comp.file.startswith("http"):
                    image_file_path = await download_image_by_url(comp.file)
-                    file_path = image_file_path
+                    file_path = image_file_path if image_file_path else ""
                elif comp.file and comp.file.startswith("base64://"):
                    base64_str = comp.file.removeprefix("base64://")
                    image_data = base64.b64decode(base64_str)
@@ -54,10 +62,17 @@ class LarkMessageEvent(AstrMessageEvent):
                    with open(file_path, "wb") as f:
                        f.write(BytesIO(image_data).getvalue())
                else:
-                    file_path = comp.file
+                    file_path = comp.file if comp.file else ""

                if image_file is None:
-                    image_file = open(file_path, "rb")
+                    if not file_path:
+                        logger.error("[Lark] 图片路径为空，无法上传")
+                        continue
+                    try:
+                        image_file = open(file_path, "rb")
+                    except Exception as e:
+                        logger.error(f"[Lark] 无法打开图片文件: {e}")
+                        continue

                request = (
                    CreateImageRequest.builder()
@@ -69,9 +84,20 @@ class LarkMessageEvent(AstrMessageEvent):
                    )
                    .build()
                )
+
+                if lark_client.im is None:
+                    logger.error("[Lark] API Client im 模块未初始化，无法上传图片")
+                    continue
+
                response = await lark_client.im.v1.image.acreate(request)
                if not response.success():
                    logger.error(f"无法上传飞书图片({response.code}): {response.msg}")
+                    continue
+
+                if response.data is None:
+                    logger.error("[Lark] 上传图片成功但未返回数据(data is None)")
+                    continue
+
                image_key = response.data.image_key
                logger.debug(image_key)
                ret.append(_stage)
@@ -107,6 +133,10 @@ class LarkMessageEvent(AstrMessageEvent):
            .build()
        )

+        if self.bot.im is None:
+            logger.error("[Lark] API Client im 模块未初始化，无法回复消息")
+            return
+
        response = await self.bot.im.v1.message.areply(request)

        if not response.success():
@@ -115,6 +145,10 @@ class LarkMessageEvent(AstrMessageEvent):
        await super().send(message)

    async def react(self, emoji: str):
+        if self.bot.im is None:
+            logger.error("[Lark] API Client im 模块未初始化，无法发送表情")
+            return
+
        request = (
            CreateMessageReactionRequest.builder()
            .message_id(self.message_obj.message_id)
@@ -125,6 +159,7 @@ class LarkMessageEvent(AstrMessageEvent):
            )
            .build()
        )
+
        response = await self.bot.im.v1.message_reaction.acreate(request)
        if not response.success():
            logger.error(f"发送飞书表情回应失败({response.code}): {response.msg}")
@@ -0,0 +1,206 @@
+"""飞书(Lark) Webhook 服务器实现
+
+实现飞书事件订阅的 Webhook 模式，支持:
+1. 请求 URL 验证 (challenge 验证)
+2. 事件加密/解密 (AES-256-CBC)
+3. 签名校验 (SHA256)
+4. 事件接收和处理
+"""
+
+import asyncio
+import base64
+import hashlib
+import json
+from collections.abc import Awaitable, Callable
+
+from Crypto.Cipher import AES
+
+from astrbot.api import logger
+
+
+class AESCipher:
+    """AES 加密/解密工具类"""
+
+    def __init__(self, key: str):
+        self.bs = AES.block_size
+        self.key = hashlib.sha256(self.str_to_bytes(key)).digest()
+
+    @staticmethod
+    def str_to_bytes(data):
+        u_type = type(b"".decode("utf8"))
+        if isinstance(data, u_type):
+            return data.encode("utf8")
+        return data
+
+    @staticmethod
+    def _unpad(s):
+        return s[: -ord(s[len(s) - 1 :])]
+
+    def decrypt(self, enc):
+        iv = enc[: AES.block_size]
+        cipher = AES.new(self.key, AES.MODE_CBC, iv)
+        return self._unpad(cipher.decrypt(enc[AES.block_size :]))
+
+    def decrypt_string(self, enc):
+        enc = base64.b64decode(enc)
+        return self.decrypt(enc).decode("utf8")
+
+
+class LarkWebhookServer:
+    """飞书 Webhook 服务器
+
+    仅支持统一 Webhook 模式
+    """
+
+    def __init__(self, config: dict, event_queue: asyncio.Queue):
+        """初始化 Webhook 服务器
+
+        Args:
+            config: 飞书配置
+            event_queue: 事件队列
+        """
+        self.app_id = config["app_id"]
+        self.app_secret = config["app_secret"]
+        self.encrypt_key = config.get("lark_encrypt_key", "")
+        self.verification_token = config.get("lark_verification_token", "")
+
+        self.event_queue = event_queue
+        self.callback: Callable[[dict], Awaitable[None]] | None = None
+
+        # 初始化加密工具
+        self.cipher = None
+        if self.encrypt_key:
+            self.cipher = AESCipher(self.encrypt_key)
+
+    def verify_signature(
+        self,
+        timestamp: str,
+        nonce: str,
+        encrypt_key: str,
+        body: bytes,
+        signature: str,
+    ) -> bool:
+        """验证签名
+
+        Args:
+            timestamp: 请求时间戳
+            nonce: 随机数
+            encrypt_key: 加密密钥
+            body: 请求体
+            signature: 签名
+
+        Returns:
+            签名是否有效
+        """
+        # 拼接字符串: timestamp + nonce + encrypt_key + body
+        bytes_b1 = (timestamp + nonce + encrypt_key).encode("utf-8")
+        bytes_b = bytes_b1 + body
+        h = hashlib.sha256(bytes_b)
+        calculated_signature = h.hexdigest()
+        return calculated_signature == signature
+
+    def decrypt_event(self, encrypted_data: str) -> dict:
+        """解密事件数据
+
+        Args:
+            encrypted_data: 加密的事件数据
+
+        Returns:
+            解密后的事件字典
+        """
+        if not self.cipher:
+            raise ValueError("未配置 encrypt_key，无法解密事件")
+
+        decrypted_str = self.cipher.decrypt_string(encrypted_data)
+        return json.loads(decrypted_str)
+
+    async def handle_challenge(self, event_data: dict) -> dict:
+        """处理 challenge 验证请求
+
+        Args:
+            event_data: 事件数据
+
+        Returns:
+            包含 challenge 的响应
+        """
+        challenge = event_data.get("challenge", "")
+        logger.info(f"[Lark Webhook] 收到 challenge 验证请求: {challenge}")
+
+        return {"challenge": challenge}
+
+    async def handle_callback(self, request) -> tuple[dict, int] | dict:
+        """处理 webhook 回调，可被统一 webhook 入口复用
+
+        Args:
+            request: Quart 请求对象
+
+        Returns:
+            响应数据
+        """
+        # 获取原始请求体
+        body = await request.get_data()
+
+        try:
+            event_data = await request.json
+        except Exception as e:
+            logger.error(f"[Lark Webhook] 解析请求体失败: {e}")
+            return {"error": "Invalid JSON"}, 400
+
+        if not event_data:
+            logger.error("[Lark Webhook] 请求体为空")
+            return {"error": "Empty request body"}, 400
+
+        # 如果配置了 encrypt_key，进行签名验证
+        if self.encrypt_key:
+            timestamp = request.headers.get("X-Lark-Request-Timestamp", "")
+            nonce = request.headers.get("X-Lark-Request-Nonce", "")
+            signature = request.headers.get("X-Lark-Signature", "")
+
+            if timestamp and nonce and signature:
+                if not self.verify_signature(
+                    timestamp, nonce, self.encrypt_key, body, signature
+                ):
+                    logger.error("[Lark Webhook] 签名验证失败")
+                    return {"error": "Invalid signature"}, 401
+
+        # 检查是否是加密事件
+        if "encrypt" in event_data:
+            try:
+                event_data = self.decrypt_event(event_data["encrypt"])
+                logger.debug(f"[Lark Webhook] 解密后的事件: {event_data}")
+            except Exception as e:
+                logger.error(f"[Lark Webhook] 解密事件失败: {e}")
+                return {"error": "Decryption failed"}, 400
+
+        # 验证 token
+        if self.verification_token:
+            header = event_data.get("header", {})
+            if header:
+                token = header.get("token", "")
+            else:
+                token = event_data.get("token", "")
+            if token != self.verification_token:
+                logger.error("[Lark Webhook] Verification Token 不匹配。")
+                return {"error": "Invalid verification token"}, 401
+
+        # 处理 URL 验证 (challenge)
+        if event_data.get("type") == "url_verification":
+            return await self.handle_challenge(event_data)
+
+        # 调用回调函数处理事件
+        if self.callback:
+            try:
+                await self.callback(event_data)
+            except Exception as e:
+                logger.error(f"[Lark Webhook] 处理事件回调失败: {e}", exc_info=True)
+                return {"error": "Event processing failed"}, 500
+
+        return {}
+
+    def set_callback(self, callback: Callable[[dict], Awaitable[None]]):
+        """设置事件回调函数
+
+        Args:
+            callback: 处理事件的异步函数
+        """
+        self.callback = callback
@@ -1,7 +1,6 @@
 import asyncio
 import os
 import random
-from collections.abc import Awaitable
 from typing import Any

 import astrbot.api.message_components as Comp
@@ -45,7 +44,9 @@ MAX_FILE_UPLOAD_COUNT = 16
 DEFAULT_UPLOAD_CONCURRENCY = 3


-@register_platform_adapter("misskey", "Misskey 平台适配器")
+@register_platform_adapter(
+    "misskey", "Misskey 平台适配器", support_streaming_message=False
+)
 class MisskeyPlatformAdapter(Platform):
    def __init__(
        self,
@@ -53,8 +54,7 @@ class MisskeyPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-        self.config = platform_config or {}
+        super().__init__(platform_config or {}, event_queue)
        self.settings = platform_settings or {}
        self.instance_url = self.config.get("misskey_instance_url", "")
        self.access_token = self.config.get("misskey_token", "")
@@ -120,6 +120,7 @@ class MisskeyPlatformAdapter(Platform):
            description="Misskey 平台适配器",
            id=self.config.get("id", "misskey"),
            default_config_tmpl=default_config,
+            support_streaming_message=False,
        )

    async def run(self):
@@ -201,7 +202,7 @@ class MisskeyPlatformAdapter(Platform):
            if not isinstance(message.raw_message, dict):
                message.raw_message = {}
            message.raw_message["poll"] = poll
-            message.poll = poll
+            message.__setattr__("poll", poll)
        except Exception:
            pass

@@ -370,7 +371,7 @@ class MisskeyPlatformAdapter(Platform):
        self,
        session: MessageSession,
        message_chain: MessageChain,
-    ) -> Awaitable[Any]:
+    ) -> None:
        if not self.api:
            logger.error("[Misskey] API 客户端未初始化")
            return await super().send_by_session(session, message_chain)
@@ -3,6 +3,7 @@ import base64
 import os
 import random
 import uuid
+from typing import cast

 import aiofiles
 import botpy
@@ -60,7 +61,10 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                    time_since_last_edit = current_time - last_edit_time

                    if time_since_last_edit >= throttle_interval:
-                        ret = await self._post_send(stream=stream_payload)
+                        ret = cast(
+                            message.Message,
+                            await self._post_send(stream=stream_payload),
+                        )
                        stream_payload["index"] += 1
                        stream_payload["id"] = ret["id"]
                        last_edit_time = asyncio.get_event_loop().time()
@@ -69,6 +73,8 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                # 结束流式对话，并且传输 buffer 中剩余的消息
                stream_payload["state"] = 10
                ret = await self._post_send(stream=stream_payload)
+            else:
+                ret = await self._post_send()

        except Exception as e:
            logger.error(f"发送流式消息时出错: {e}", exc_info=True)
@@ -81,7 +87,8 @@ class QQOfficialMessageEvent(AstrMessageEvent):
            return None

        source = self.message_obj.raw_message
-        assert isinstance(
+
+        if not isinstance(
            source,
            (
                botpy.message.Message,
@@ -89,7 +96,9 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                botpy.message.DirectMessage,
                botpy.message.C2CMessage,
            ),
-        )
+        ):
+            logger.warning(f"[QQOfficial] 不支持的消息源类型: {type(source)}")
+            return None

        (
            plain_text,
@@ -106,7 +115,7 @@ class QQOfficialMessageEvent(AstrMessageEvent):
        ):
            return None

-        payload = {
+        payload: dict = {
            "content": plain_text,
            "msg_id": self.message_obj.message_id,
        }
@@ -116,8 +125,12 @@ class QQOfficialMessageEvent(AstrMessageEvent):

        ret = None

-        match type(source):
-            case botpy.message.GroupMessage:
+        match source:
+            case botpy.message.GroupMessage():
+                if not source.group_openid:
+                    logger.error("[QQOfficial] GroupMessage 缺少 group_openid")
+                    return None
+
                if image_base64:
                    media = await self.upload_group_and_c2c_image(
                        image_base64,
@@ -138,7 +151,8 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                    group_openid=source.group_openid,
                    **payload,
                )
-            case botpy.message.C2CMessage:
+
+            case botpy.message.C2CMessage():
                if image_base64:
                    media = await self.upload_group_and_c2c_image(
                        image_base64,
@@ -167,18 +181,23 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                        **payload,
                    )
                logger.debug(f"Message sent to C2C: {ret}")
-            case botpy.message.Message:
+
+            case botpy.message.Message():
                if image_path:
                    payload["file_image"] = image_path
                ret = await self.bot.api.post_message(
                    channel_id=source.channel_id,
                    **payload,
                )
-            case botpy.message.DirectMessage:
+
+            case botpy.message.DirectMessage():
                if image_path:
                    payload["file_image"] = image_path
                ret = await self.bot.api.post_dms(guild_id=source.guild_id, **payload)

+            case _:
+                pass
+
        await super().send(self.send_buffer)

        self.send_buffer = None
@@ -196,18 +215,33 @@ class QQOfficialMessageEvent(AstrMessageEvent):
            "file_type": file_type,
            "srv_send_msg": False,
        }
+
+        result = None
        if "openid" in kwargs:
            payload["openid"] = kwargs["openid"]
            route = Route("POST", "/v2/users/{openid}/files", openid=kwargs["openid"])
-            return await self.bot.api._http.request(route, json=payload)
-        if "group_openid" in kwargs:
+            result = await self.bot.api._http.request(route, json=payload)
+        elif "group_openid" in kwargs:
            payload["group_openid"] = kwargs["group_openid"]
            route = Route(
                "POST",
                "/v2/groups/{group_openid}/files",
                group_openid=kwargs["group_openid"],
            )
-            return await self.bot.api._http.request(route, json=payload)
+            result = await self.bot.api._http.request(route, json=payload)
+        else:
+            raise ValueError("Invalid upload parameters")
+
+        if not isinstance(result, dict):
+            raise RuntimeError(
+                f"Failed to upload image, response is not dict: {result}"
+            )
+
+        return Media(
+            file_uuid=result["file_uuid"],
+            file_info=result["file_info"],
+            ttl=result.get("ttl", 0),
+        )

    async def upload_group_and_c2c_record(
        self,
@@ -250,11 +284,14 @@ class QQOfficialMessageEvent(AstrMessageEvent):
            result = await self.bot.api._http.request(route, json=payload)

            if result:
+                if not isinstance(result, dict):
+                    logger.error(f"上传文件响应格式错误: {result}")
+                    return None
+
                return Media(
-                    file_uuid=result.get("file_uuid"),
-                    file_info=result.get("file_info"),
+                    file_uuid=result["file_uuid"],
+                    file_info=result["file_info"],
                    ttl=result.get("ttl", 0),
-                    file_id=result.get("id", ""),
                )
        except Exception as e:
            logger.error(f"上传请求错误: {e}")
@@ -271,7 +308,7 @@ class QQOfficialMessageEvent(AstrMessageEvent):
        message_reference: message.Reference | None = None,
        media: message.Media | None = None,
        msg_id: str | None = None,
-        msg_seq: str = 1,
+        msg_seq: int | None = 1,
        event_id: str | None = None,
        markdown: message.MarkdownPayload | None = None,
        keyboard: message.Keyboard | None = None,
@@ -280,7 +317,14 @@ class QQOfficialMessageEvent(AstrMessageEvent):
        payload = locals()
        payload.pop("self", None)
        route = Route("POST", "/v2/users/{openid}/messages", openid=openid)
-        return await self.bot.api._http.request(route, json=payload)
+        result = await self.bot.api._http.request(route, json=payload)
+
+        if not isinstance(result, dict):
+            raise RuntimeError(
+                f"Failed to post c2c message, response is not dict: {result}"
+            )
+
+        return message.Message(**result)

    @staticmethod
    async def _parse_to_qqofficial(message: MessageChain):
@@ -300,8 +344,10 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                    image_base64 = file_to_base64(image_file_path)
                elif i.file and i.file.startswith("base64://"):
                    image_base64 = i.file
-                else:
+                elif i.file:
                    image_base64 = file_to_base64(i.file)
+                else:
+                    raise ValueError("Unsupported image file format")
                image_base64 = image_base64.removeprefix("base64://")
            elif isinstance(i, Record):
                if i.file:
@@ -4,6 +4,7 @@ import asyncio
 import logging
 import os
 import time
+from typing import cast

 import botpy
 import botpy.message
@@ -44,7 +45,9 @@ class botClient(Client):
            MessageType.GROUP_MESSAGE,
        )
        abm.session_id = (
-            abm.sender.user_id if self.platform.unique_session else message.group_openid
+            abm.sender.user_id
+            if self.platform.unique_session
+            else cast(str, message.group_openid)
        )
        self._commit(abm)

@@ -97,13 +100,11 @@ class QQOfficialPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)

        self.appid = platform_config["appid"]
        self.secret = platform_config["secret"]
-        self.unique_session = platform_settings["unique_session"]
+        self.unique_session: bool = platform_settings["unique_session"]
        qq_group = platform_config["enable_group_c2c"]
        guild_dm = platform_config["enable_guild_direct_message"]

@@ -139,12 +140,15 @@ class QQOfficialPlatformAdapter(Platform):
        return PlatformMetadata(
            name="qq_official",
            description="QQ 机器人官方 API 适配器",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
        )

    @staticmethod
    def _parse_from_qqofficial(
-        message: botpy.message.Message | botpy.message.GroupMessage,
+        message: botpy.message.Message
+        | botpy.message.GroupMessage
+        | botpy.message.DirectMessage
+        | botpy.message.C2CMessage,
        message_type: MessageType,
    ):
        abm = AstrBotMessage()
@@ -152,7 +156,7 @@ class QQOfficialPlatformAdapter(Platform):
        abm.timestamp = int(time.time())
        abm.raw_message = message
        abm.message_id = message.id
-        abm.tag = "qq_official"
+        # abm.tag = "qq_official"
        msg: list[BaseMessageComponent] = []

        if isinstance(message, botpy.message.GroupMessage) or isinstance(
@@ -182,9 +186,9 @@ class QQOfficialPlatformAdapter(Platform):
            message,
            botpy.message.DirectMessage,
        ):
-            try:
+            if isinstance(message, botpy.message.Message):
                abm.self_id = str(message.mentions[0].id)
-            except BaseException as _:
+            else:
                abm.self_id = ""

            plain_content = message.content.replace(
@@ -1,5 +1,6 @@
 import asyncio
 import logging
+from typing import Any, cast

 import botpy
 import botpy.message
@@ -11,6 +12,7 @@ from astrbot import logger
 from astrbot.api.event import MessageChain
 from astrbot.api.platform import AstrBotMessage, MessageType, Platform, PlatformMetadata
 from astrbot.core.platform.astr_message_event import MessageSesion
+from astrbot.core.utils.webhook_utils import log_webhook_info

 from ...register import register_platform_adapter
 from ..qqofficial.qqofficial_platform_adapter import QQOfficialPlatformAdapter
@@ -34,7 +36,9 @@ class botClient(Client):
            MessageType.GROUP_MESSAGE,
        )
        abm.session_id = (
-            abm.sender.user_id if self.platform.unique_session else message.group_openid
+            abm.sender.user_id
+            if self.platform.unique_session
+            else cast(str, message.group_openid)
        )
        self._commit(abm)

@@ -87,13 +91,12 @@ class QQOfficialWebhookPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)

        self.appid = platform_config["appid"]
        self.secret = platform_config["secret"]
        self.unique_session = platform_settings["unique_session"]
+        self.unified_webhook_mode = platform_config.get("unified_webhook_mode", False)

        intents = botpy.Intents(
            public_messages=True,
@@ -106,6 +109,7 @@ class QQOfficialWebhookPlatformAdapter(Platform):
            timeout=20,
        )
        self.client.set_platform(self)
+        self.webhook_helper = None

    async def send_by_session(
        self,
@@ -118,7 +122,7 @@ class QQOfficialWebhookPlatformAdapter(Platform):
        return PlatformMetadata(
            name="qq_official_webhook",
            description="QQ 机器人官方 API 适配器",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
        )

    async def run(self):
@@ -128,16 +132,37 @@ class QQOfficialWebhookPlatformAdapter(Platform):
            self.client,
        )
        await self.webhook_helper.initialize()
-        await self.webhook_helper.start_polling()
+
+        # 如果启用统一 webhook 模式，则不启动独立服务器
+        webhook_uuid = self.config.get("webhook_uuid")
+        if self.unified_webhook_mode and webhook_uuid:
+            log_webhook_info(f"{self.meta().id}(QQ 官方机器人 Webhook)", webhook_uuid)
+            # 保持运行状态，等待 shutdown
+            await self.webhook_helper.shutdown_event.wait()
+        else:
+            await self.webhook_helper.start_polling()

    def get_client(self) -> botClient:
        return self.client

+    async def webhook_callback(self, request: Any) -> Any:
+        """统一 Webhook 回调入口"""
+        if not self.webhook_helper:
+            return {"error": "Webhook helper not initialized"}, 500
+
+        # 复用 webhook_helper 的回调处理逻辑
+        return await self.webhook_helper.handle_callback(request)
+
    async def terminate(self):
-        self.webhook_helper.shutdown_event.set()
+        if self.webhook_helper:
+            self.webhook_helper.shutdown_event.set()
        await self.client.close()
-        try:
-            await self.webhook_helper.server.shutdown()
-        except Exception as _:
-            pass
+        if self.webhook_helper and not self.unified_webhook_mode:
+            try:
+                await self.webhook_helper.server.shutdown()
+            except Exception as exc:
+                logger.warning(
+                    f"Exception occurred during QQOfficialWebhook server shutdown: {exc}",
+                    exc_info=True,
+                )
        logger.info("QQ 机器人官方 API 适配器已经被优雅地关闭")
@@ -1,5 +1,6 @@
 import asyncio
 import logging
+from typing import cast

 import quart
 from botpy import BotAPI, BotHttp, BotWebSocket, Client, ConnectionSession, Token
@@ -78,7 +79,19 @@ class QQOfficialWebhook:
        return response

    async def callback(self):
-        msg: dict = await quart.request.json
+        """内部服务器的回调入口"""
+        return await self.handle_callback(quart.request)
+
+    async def handle_callback(self, request) -> dict:
+        """处理 webhook 回调，可被统一 webhook 入口复用
+
+        Args:
+            request: Quart 请求对象
+
+        Returns:
+            响应数据
+        """
+        msg: dict = await request.json
        logger.debug(f"收到 qq_official_webhook 回调: {msg}")

        event = msg.get("t")
@@ -87,7 +100,7 @@ class QQOfficialWebhook:

        if opcode == 13:
            # validation
-            signed = await self.webhook_validation(data)
+            signed = await self.webhook_validation(cast(dict, data))
            print(signed)
            return signed

@@ -29,8 +29,7 @@ from astrbot.core.platform.astr_message_event import MessageSession


@register_platform_adapter(
-    "satori",
-    "Satori 协议适配器",
+    "satori", "Satori 协议适配器", support_streaming_message=False
 )
 class SatoriPlatformAdapter(Platform):
    def __init__(
@@ -39,8 +38,7 @@ class SatoriPlatformAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)
        self.settings = platform_settings

        self.api_base_url = self.config.get(
@@ -60,6 +58,7 @@ class SatoriPlatformAdapter(Platform):
            name="satori",
            description="Satori 通用协议适配器",
            id=self.config["id"],
+            support_streaming_message=False,
        )

        self.ws: ClientConnection | None = None
@@ -4,9 +4,11 @@ import hmac
 import json
 import logging
 from collections.abc import Callable
+from typing import cast

 from quart import Quart, Response, request
 from slack_sdk.socket_mode.aiohttp import SocketModeClient
+from slack_sdk.socket_mode.async_client import AsyncBaseSocketModeClient
 from slack_sdk.socket_mode.request import SocketModeRequest
 from slack_sdk.socket_mode.response import SocketModeResponse
 from slack_sdk.web.async_client import AsyncWebClient
@@ -47,51 +49,62 @@ class SlackWebhookClient:

        @self.app.route(self.path, methods=["POST"])
        async def slack_events():
-            """处理 Slack 事件"""
-            try:
-                # 获取请求体和头部
-                body = await request.get_data()
-                event_data = json.loads(body.decode("utf-8"))
-
-                # Verify Slack request signature
-                timestamp = request.headers.get("X-Slack-Request-Timestamp")
-                signature = request.headers.get("X-Slack-Signature")
-                if not timestamp or not signature:
-                    return Response("Missing headers", status=400)
-                # Calculate the HMAC signature
-                sig_basestring = f"v0:{timestamp}:{body.decode('utf-8')}"
-                my_signature = (
-                    "v0="
-                    + hmac.new(
-                        self.signing_secret.encode("utf-8"),
-                        sig_basestring.encode("utf-8"),
-                        hashlib.sha256,
-                    ).hexdigest()
-                )
-                # Verify the signature
-                if not hmac.compare_digest(my_signature, signature):
-                    logger.warning("Slack request signature verification failed")
-                    return Response("Invalid signature", status=400)
-                logger.info(f"Received Slack event: {event_data}")
-
-                # 处理 URL 验证事件
-                if event_data.get("type") == "url_verification":
-                    return {"challenge": event_data.get("challenge")}
-                # 处理事件
-                if self.event_handler and event_data.get("type") == "event_callback":
-                    await self.event_handler(event_data)
-
-                return Response("", status=200)
-
-            except Exception as e:
-                logger.error(f"处理 Slack 事件时出错: {e}")
-                return Response("Internal Server Error", status=500)
+            """内部服务器的 POST 回调入口"""
+            return await self.handle_callback(request)

        @self.app.route("/health", methods=["GET"])
        async def health_check():
            """健康检查端点"""
            return {"status": "ok", "service": "slack-webhook"}

+    async def handle_callback(self, req):
+        """处理 Slack 回调请求，可被统一 webhook 入口复用
+
+        Args:
+            req: Quart 请求对象
+
+        Returns:
+            Response 对象或字典
+        """
+        try:
+            # 获取请求体和头部
+            body = cast(bytes, await req.get_data())
+            event_data = json.loads(body.decode("utf-8"))
+
+            # Verify Slack request signature
+            timestamp = req.headers.get("X-Slack-Request-Timestamp")
+            signature = req.headers.get("X-Slack-Signature")
+            if not timestamp or not signature:
+                return Response("Missing headers", status=400)
+            # Calculate the HMAC signature
+            sig_basestring = f"v0:{timestamp}:{body.decode('utf-8')}"
+            my_signature = (
+                "v0="
+                + hmac.new(
+                    self.signing_secret.encode("utf-8"),
+                    sig_basestring.encode("utf-8"),
+                    hashlib.sha256,
+                ).hexdigest()
+            )
+            # Verify the signature
+            if not hmac.compare_digest(my_signature, signature):
+                logger.warning("Slack request signature verification failed")
+                return Response("Invalid signature", status=400)
+            logger.info(f"Received Slack event: {event_data}")
+
+            # 处理 URL 验证事件
+            if event_data.get("type") == "url_verification":
+                return {"challenge": event_data.get("challenge")}
+            # 处理事件
+            if self.event_handler and event_data.get("type") == "event_callback":
+                await self.event_handler(event_data)
+
+            return Response("", status=200)
+
+        except Exception as e:
+            logger.error(f"处理 Slack 事件时出错: {e}")
+            return Response("Internal Server Error", status=500)
+
    async def start(self):
        """启动 Webhook 服务器"""
        logger.info(
@@ -128,9 +141,14 @@ class SlackSocketClient:
        self.event_handler = event_handler
        self.socket_client = None

-    async def _handle_events(self, _: SocketModeClient, req: SocketModeRequest):
+    async def _handle_events(
+        self, _: AsyncBaseSocketModeClient, req: SocketModeRequest
+    ):
        """处理 Socket Mode 事件"""
        try:
+            if self.socket_client is None:
+                raise RuntimeError("Socket client is not initialized")
+
            # 确认收到事件
            response = SocketModeResponse(envelope_id=req.envelope_id)
            await self.socket_client.send_socket_mode_response(response)
@@ -3,8 +3,7 @@ import base64
 import re
 import time
 import uuid
-from collections.abc import Awaitable
-from typing import Any
+from typing import Any, cast

 import aiohttp
 from slack_sdk.socket_mode.request import SocketModeRequest
@@ -21,6 +20,7 @@ from astrbot.api.platform import (
    PlatformMetadata,
 )
 from astrbot.core.platform.astr_message_event import MessageSesion
+from astrbot.core.utils.webhook_utils import log_webhook_info

 from ...register import register_platform_adapter
 from .client import SlackSocketClient, SlackWebhookClient
@@ -30,6 +30,7 @@ from .slack_event import SlackMessageEvent
@register_platform_adapter(
    "slack",
    "适用于 Slack 的消息平台适配器，支持 Socket Mode 和 Webhook Mode。",
+    support_streaming_message=False,
 )
 class SlackAdapter(Platform):
    def __init__(
@@ -38,9 +39,7 @@ class SlackAdapter(Platform):
        platform_settings: dict,
        event_queue: asyncio.Queue,
    ) -> None:
-        super().__init__(event_queue)
-
-        self.config = platform_config
+        super().__init__(platform_config, event_queue)
        self.settings = platform_settings
        self.unique_session = platform_settings.get("unique_session", False)

@@ -48,6 +47,7 @@ class SlackAdapter(Platform):
        self.app_token = platform_config.get("app_token")
        self.signing_secret = platform_config.get("signing_secret")
        self.connection_mode = platform_config.get("slack_connection_mode", "socket")
+        self.unified_webhook_mode = platform_config.get("unified_webhook_mode", False)
        self.webhook_host = platform_config.get("slack_webhook_host", "0.0.0.0")
        self.webhook_port = platform_config.get("slack_webhook_port", 3000)
        self.webhook_path = platform_config.get(
@@ -67,7 +67,8 @@ class SlackAdapter(Platform):
        self.metadata = PlatformMetadata(
            name="slack",
            description="适用于 Slack 的消息平台适配器，支持 Socket Mode 和 Webhook Mode。",
-            id=self.config.get("id"),
+            id=cast(str, self.config.get("id")),
+            support_streaming_message=False,
        )

        # 初始化 Slack Web Client
@@ -116,13 +117,13 @@ class SlackAdapter(Platform):
        logger.debug(f"[slack] RawMessage {event}")

        abm = AstrBotMessage()
-        abm.self_id = self.bot_self_id
+        abm.self_id = cast(str, self.bot_self_id)

        # 获取用户信息
        user_id = event.get("user", "")
        try:
            user_info = await self.web_client.users_info(user=user_id)
-            user_data = user_info["user"]
+            user_data = cast(dict, user_info["user"])
            user_name = user_data.get("real_name") or user_data.get("name", user_id)
        except Exception:
            user_name = user_id
@@ -133,7 +134,7 @@ class SlackAdapter(Platform):
        channel_id = event.get("channel", "")
        try:
            channel_info = await self.web_client.conversations_info(channel=channel_id)
-            is_im = channel_info["channel"]["is_im"]
+            is_im = cast(dict, channel_info["channel"])["is_im"]

            if is_im:
                abm.type = MessageType.FRIEND_MESSAGE
@@ -176,7 +177,7 @@ class SlackAdapter(Platform):
                for mention in mentions:
                    try:
                        mentioned_user = await self.web_client.users_info(user=mention)
-                        user_data = mentioned_user["user"]
+                        user_data = cast(dict, mentioned_user["user"])
                        user_name = user_data.get("real_name") or user_data.get(
                            "name",
                            mention,
@@ -327,7 +328,7 @@ class SlackAdapter(Platform):
                )
                raise Exception(f"下载文件失败: {resp.status}")

-    async def run(self) -> Awaitable[Any]:
+    async def run(self) -> None:
        self.bot_self_id = await self.get_bot_user_id()
        logger.info(f"Slack auth test OK. Bot ID: {self.bot_self_id}")

@@ -359,10 +360,17 @@ class SlackAdapter(Platform):
                self._handle_webhook_event,
            )

-            logger.info(
-                f"Slack 适配器 (Webhook Mode) 启动中，监听 {self.webhook_host}:{self.webhook_port}{self.webhook_path}...",
-            )
-            await self.webhook_client.start()
+            # 如果启用统一 webhook 模式，则不启动独立服务器
+            webhook_uuid = self.config.get("webhook_uuid")
+            if self.unified_webhook_mode and webhook_uuid:
+                log_webhook_info(f"{self.meta().id}(Slack)", webhook_uuid)
+                # 保持运行状态，等待 shutdown
+                await self.webhook_client.shutdown_event.wait()
+            else:
+                logger.info(
+                    f"Slack 适配器 (Webhook Mode) 启动中，监听 {self.webhook_host}:{self.webhook_port}{self.webhook_path}...",
+                )
+                await self.webhook_client.start()

        else:
            raise ValueError(
@@ -389,12 +397,19 @@ class SlackAdapter(Platform):
            if abm:
                await self.handle_msg(abm)

+    async def webhook_callback(self, request: Any) -> Any:
+        """统一 Webhook 回调入口"""
+        if self.connection_mode != "webhook" or not self.webhook_client:
+            return {"error": "Slack adapter is not in webhook mode"}, 400
+
+        return await self.webhook_client.handle_callback(request)
+
    async def terminate(self):
        if self.socket_client:
            await self.socket_client.stop()
        if self.webhook_client:
            await self.webhook_client.stop()
-        logger.info("Slack 适配器已被优雅地关闭")
+        logger.info("Slack 适配器已被关闭")

    def meta(self) -> PlatformMetadata:
        return self.metadata
@@ -412,3 +427,10 @@ class SlackAdapter(Platform):

    def get_client(self):
        return self.web_client
+
+    def unified_webhook(self) -> bool:
+        return bool(
+            self.config.get("unified_webhook_mode", False)
+            and self.config.get("slack_connection_mode", "") == "webhook"
+            and self.config.get("webhook_uuid")
+        )
@@ -1,6 +1,7 @@
 import asyncio
 import re
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncGenerator, Iterable
+from typing import cast

 from slack_sdk.web.async_client import AsyncWebClient

@@ -31,14 +32,14 @@ class SlackMessageEvent(AstrMessageEvent):
    async def _from_segment_to_slack_block(
        segment: BaseMessageComponent,
        web_client: AsyncWebClient,
-    ) -> dict:
+    ) -> dict | None:
        """将消息段转换为 Slack 块格式"""
        if isinstance(segment, Plain):
            return {"type": "section", "text": {"type": "mrkdwn", "text": segment.text}}
        if isinstance(segment, Image):
            # upload file
            url = segment.url or segment.file
-            if url.startswith("http"):
+            if url and url.startswith("http"):
                return {
                    "type": "image",
                    "image_url": url,
@@ -55,7 +56,7 @@ class SlackMessageEvent(AstrMessageEvent):
                    "type": "section",
                    "text": {"type": "mrkdwn", "text": "图片上传失败"},
                }
-            image_url = response["files"][0]["url_private"]
+            image_url = cast(list, response["files"])[0]["url_private"]
            logger.debug(f"Slack file upload response: {response}")
            return {
                "type": "image",
@@ -77,7 +78,7 @@ class SlackMessageEvent(AstrMessageEvent):
                    "type": "section",
                    "text": {"type": "mrkdwn", "text": "文件上传失败"},
                }
-            file_url = response["files"][0]["permalink"]
+            file_url = cast(list, response["files"])[0]["permalink"]
            return {
                "type": "section",
                "text": {
@@ -85,7 +86,6 @@ class SlackMessageEvent(AstrMessageEvent):
                    "text": f"文件: <{file_url}|{segment.name or '文件'}>",
                },
            }
-        return {"type": "section", "text": {"type": "mrkdwn", "text": str(segment)}}

    @staticmethod
    async def _parse_slack_blocks(
@@ -115,7 +115,8 @@ class SlackMessageEvent(AstrMessageEvent):
                    segment,
                    web_client,
                )
-                blocks.append(block)
+                if block:
+                    blocks.append(block)

        # 如果最后还有文本内容
        if text_content.strip():
@@ -225,10 +226,10 @@ class SlackMessageEvent(AstrMessageEvent):
            )

            members = []
-            for member_id in members_response["members"]:
+            for member_id in cast(Iterable, members_response["members"]):
                try:
                    user_info = await self.web_client.users_info(user=member_id)
-                    user_data = user_info["user"]
+                    user_data = cast(dict, user_info["user"])
                    members.append(
                        MessageMember(
                            user_id=member_id,
@@ -240,7 +241,7 @@ class SlackMessageEvent(AstrMessageEvent):
                    # 如果获取用户信息失败，使用默认信息
                    members.append(MessageMember(user_id=member_id, nickname=member_id))

-            channel_data = channel_info["channel"]
+            channel_data = cast(dict, channel_info["channel"])
            return Group(
                group_id=channel_id,
                group_name=channel_data.get("name", ""),
--- a/Show More
+++ b/Show More